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MONITORING AND MAPPING ASIAN CITRUS PSYLLID USING SHAKING MACHINE
By
MUNA JAMIL ABBAS
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2017
© 2017 Muna Jamil Abbas
To my husband and my parents
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ACKNOWLEDGMENTS
A sincere praise to the Allah, who illuminated the path for me, opened the doors
of knowledge and gave me the patience to complete my graduate studies and research
work. I would like to thank my advisor, Dr. Reza Ehsani, professor of Agricultural and
Biological Engineering, University of Florida for the opportunity of pursuing a doctoral
degree and for his continuous support during my doctoral study.
Also, I would like to thank my supervisory committee members, Dr. Ray Bucklin,
professor of Agricultural and Biological Engineering, University of Florida, Dr. Won Suk
“Daniel” Lee, professor of Agricultural and Biological Engineering, University of Florida,
Dr. John K. Schueller, professor of the Mechanical and Aerospace Engineering,
Dr. Kirsten Plez-Stelinski, associate professor of Entomology Department, University of
Florida for their advice, guidance during doctoral study.
I would like to thank all the staff, colleagues in Dr. Ehsaniʼs lab in Citrus
Research and Education Center in Lake Alfred, University of Florida for their help and
support.
I would like to thank the faculty, staff, and colleagues in Entomology and
Nematology Department in Citrus Research and Education Center in Lake Alfred,
University of Florida for their help and support.
I am grateful to my dear husband Salah and my lovely daughters, Iyat and
Shahad, who are the all of my life. My husband has sacrificed a lot to take care of my
daughters to let me concentrate on my studying and research. I would like to express
my gratitude to him.
My deepest words of gratitude go to my family in Iraq who always pray for my
success.
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Finally, I would like to thank the Iraqi ministry of higher education for giving me
the opportunity to attend University of Florida to pursue a doctoral degree and for their
financial support.
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TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 9
LIST OF FIGURES ........................................................................................................ 10
LIST OF ABBRIVATION ................................................................................................ 14
ABSTRACT ................................................................................................................... 15
CHAPTER
1 INTRODUCTION .................................................................................................... 17
Problem Statement ................................................................................................. 19 Specific Objectives ................................................................................................. 19
2 LITRATURE REVEIW ............................................................................................. 20
Identification of Asian Citrus Psyllid (ACP) ............................................................. 20 Ecology of Asian Citrus Psyllid ............................................................................... 21
Monitoring Methods for Asian Citrus Psyllid ............................................................ 22 Visual Sampling ................................................................................................ 23
Tap Sampling ................................................................................................... 23 Sticky Traps ...................................................................................................... 24
Sweep Nets ...................................................................................................... 27 D Vacuum ......................................................................................................... 28
Suction Traps ................................................................................................... 28 Models Proposed to Predict Insect Variation over an Area ..................................... 28
Leaf Washing Method ...................................................................................... 28 A Shake - and – Washing Technique ............................................................... 29
Two Simple Insect Sampling Devices .............................................................. 30 Washing Machine for Arthropods Recovery ..................................................... 30 Three Different Vacuum Devices for ACP Detection ........................................ 31 An Automated System for Detection and Extraction Pest in Paddy Field ......... 32
An Autonomous System for Insect Pest Detection ........................................... 33 Shaker Machine for Different Applications .............................................................. 33
Mobile Limb Shaker .......................................................................................... 33
Trunk Shaker .................................................................................................... 34 Vertical Canopy Shaker .................................................................................... 35
3 EVALUATION OF SAMPLING PATTERNS METHODS TO DETECT AND MONITOR ASIAN CITRUS PSYLLID IN CITRUS GROVES .................................. 37
Materials and Methods............................................................................................ 38
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The Study Area ................................................................................................ 38
Insect Sampling Procedures: ............................................................................ 38 Results and Discussion........................................................................................... 43
Statistical Analysis ............................................................................................ 43 Exploratory Statistics for Asian Citrus Psyllid under Different Sampling
Patterns with Different Traps Position ........................................................... 44 Comparison of the Interpolated Maps by the Two Sampling Patterns and
Two Different Traps Position ......................................................................... 46
Measures of Accuracy and Effectiveness of Prediction Maps .......................... 54
4 EVALUATION OF MEASURED ACCELERATION PRODUCED BY THE CONVENTIONALTAP METHOD AND A DEVELOPED SHAKING MACHINE FOR ASIAN CITRUS PSYLLID SAMPLING ........................................................... 56
Materials and Methods............................................................................................ 56 Tap Method Experiment ................................................................................... 56
Tap Method Experiment ................................................................................... 57 Accelerometer .................................................................................................. 60
Determination of Tree Limb Parameters........................................................... 60 Data Extraction ................................................................................................. 60 Shaking Machine Experiment ........................................................................... 63
Development of Limb Shaker ........................................................................... 63 Design and Construction .................................................................................. 63
Field Experiment .............................................................................................. 72 Data Extraction ................................................................................................. 73
Results and Discussion........................................................................................... 75 Tap Method Experiment Results ...................................................................... 75 Shaking Machine Experiment Results .............................................................. 76
5 UTILIZING A DEVELOPED SHAKING MACHINE AND DIFFERENT INTERPOLATION TECHNIQUES FOR MONITORING AND MAPPING ASIAN CITRUS PSYLLID ................................................................................................... 80
Material and Methods ............................................................................................. 82 Study Area ........................................................................................................ 82
Insect Sampling Procedure and Data Collection .............................................. 83 Zigzag Pattern Sampling: ........................................................................... 83
Sampling Technique .................................................................................. 84 Recognizing Psyllids on Sticky Traps ............................................................... 85
Geostatistical Method for Interpolating Asian Citrus Psyllid Distribution. .......... 86 Result and Discussion ............................................................................................ 87
Exploratory Statistics for Asian Citrus Psyllid under Different Sampling Patterns with Different Traps Position ........................................................... 87
Prediction of Asian Citrus Psyllid Distribution. .................................................. 87
Surface Mapping .............................................................................................. 88 Measures of Accuracy of Prediction Maps ....................................................... 88
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6 ASIAN CITRUS PSYLLID MONITORING CALCULATIONS .................................. 94
Field Capacity and Efficiency .................................................................................. 94 Comparison of the Labor Cost Using the Conventional Tap Sampling Method
Versus the Shaking Machine Method .................................................................. 95
7 CONCLUSIONS AND FUTURE WORK ................................................................. 98
Conclusions ............................................................................................................ 98 Future Work ............................................................................................................ 99
APPENDIX: MATLAB CODES .................................................................................... 101
LIST OF REFERENCES ............................................................................................. 105
BIOGRAPHICAL SKETCH .......................................................................................... 110
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LIST OF TABLES
Table page 3-1 Analysis of variance of data on the number of psyllids captured at different
trap position (horizontal and vertical). ................................................................. 43
3-2 Descriptive statistics for ACP distribution using different sampling methods ...... 44
3-3 The means of mean (M) and root mean square error (RMSE) for different traps placement under two different sampling patterns. ..................................... 45
3-4 Accuracy and effectiveness measurements for the sampling patterns methods by using different interpolation methods. ............................................. 54
4-1 Setup of the tap method experiment ................................................................... 59
4-2 Accelerometer parameters ................................................................................. 60
4-3 Tree limb parameters ......................................................................................... 60
4-4 Parameters of the limb and sensor location for the shaking machine experiment .......................................................................................................... 73
4-5 Setup of the shaking machine method experiment ............................................. 73
4-6 The output of the ANOVA analysis for shaking time and limb length using the tap method.......................................................................................................... 76
4-7 The output of the ANOVA analysis for shaking duration and shaking frequency using the shaking machine. ............................................................... 79
5-1 Descriptive statistics for ACP distribution using zigzag sampling pattern. .......... 87
5-2 The means of mean (M) and root mean square error (RMSE) for different interpolation methods. ........................................................................................ 91
5-3 RMSE, Error (%) and RMSE for different interpolation methods. ....................... 91
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LIST OF FIGURES
Figure page 1-1 Citrus greening (HLB) disease symptoms .......................................................... 18
2-1 Asian Citrus Psyllid and its three different stages ............................................... 20
2-2 Visual inspecting for the new flush ..................................................................... 23
2-3 Use of clipboard (left) and number of psyllids on white clipboard (right). ............ 24
2-4 Use the sticky traps (left) and number of Asian citrus psyllids on the traps (right) .................................................................................................................. 25
2-5 Using the sweep net for psyllid detection. .......................................................... 27
2-6 Leaf washing method filtering device. ................................................................. 29
2-7 Leaf washing machine for Arthropods recovery from plant leaves. .................... 31
2-8 Vacuum devices used for detect Asian citrus psyllid. ......................................... 32
2-9 Global architectural design. ................................................................................ 32
2-10 Trap design......................................................................................................... 33
2-11 Mobile limb shaker for apple harvester. .............................................................. 34
2-12 Olive trunk shaker ............................................................................................... 35
2-13 Shaker unit with crank drive system before adding flywheel. ............................. 36
3-1 Study area. ......................................................................................................... 38
3-2 Two different patterns of sticky traps distribution used to monitor psyllids population. .......................................................................................................... 41
3-3 Two positions for traps' placement. .................................................................... 42
3-4 Distribution of sample points representing the location of Asian Citrus Psyllid monitoring reference points. ............................................................................... 42
3-5 The plot of number of psyllids captured by different traps position ..................... 44
3-6 The cross validation comparison of the ACP distribution map by different interpolation methods using grid pattern. ............................................................ 46
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3-7 The cross validation comparison of the ACP distribution map by different interpolation methods using zigzag pattern. ....................................................... 46
3-8 Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using Inverse distance weighting method. ............................................. 48
3-9 Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using ordinary kriging method. .............................................................. 48
3-10 Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using simple kriging method. ................................................................. 49
3-11 Distribution of Asian citrus psyllid under grid pattern with vertical traps position using Inverse distance weighting method. ............................................. 49
3-12 Distribution of Asian citrus psyllid under grid pattern with vertical traps position using ordinary kriging method. .............................................................. 50
3-13 Distribution of Asian citrus psyllid under grid pattern with vertical traps position using simple kriging method. ................................................................. 50
3-14 Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps position using inverse distance weighting method. ............................................. 51
3-15 Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps position using ordinary kriging method. .............................................................. 51
3-16 Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps position using simple kriging method. ................................................................. 52
3-17 Distribution of Asian citrus psyllid under zigzag pattern with vertical traps position using inverse distance weighting method. ............................................. 52
3-18 Distribution of Asian citrus psyllid under zigzag pattern with vertical traps position using ordinary kriging method. .............................................................. 53
3-19 Distribution of Asian citrus psyllid under zigzag pattern with vertical traps position simple kriging method. .......................................................................... 53
4-1 Study area. ......................................................................................................... 57
4-2 Rod used for shaking the limbs. ......................................................................... 58
4-3 Experimental setup. ............................................................................................ 58
4-4 Circuit diagram for connecting accelerometer sensors to Arduino UNO R3 and connecting Arduino UNO R3 to a laptop via a USB cable. .......................... 59
4-5 Acceleration wavelength ..................................................................................... 62
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4-6 Statistical analysis peaks for resultant acceleration including: Histogram, Boxplot, and normal distribution. ........................................................................ 63
4-7 The front frame of the tractor attached to the main frame of the shaking machine. ............................................................................................................. 65
4-8 The components of the hydraulic system of the shaking machine:..................... 66
4-9 Top view of the transmission system of the shaking machine. ........................... 67
4-10 Top view schematic of the transmission system and the shaking system .......... 68
4-11 Rod used for shaking the limbs. ......................................................................... 68
4-12 Components of shaking system. ......................................................................... 69
4-13 Clipboard tool and its dimensions. ...................................................................... 70
4-14 Side view of the shaking machine showing the shaking arm. ............................. 71
4-15 Components of the shaking machine. ................................................................ 71
4-16 Experiment Setup ............................................................................................... 73
4-17 Statistical analysis peaks including: Histogram, Boxplot, and normal distribution. ......................................................................................................... 74
4-18 Acceleration wavelength ..................................................................................... 75
4-19 The plot of acceleration for each combination of groups of shaking time and limb length. ......................................................................................................... 77
4-20 The plot acceleration for each combination of groups of shaking time and shaking frequency. ............................................................................................. 78
5-1 Study area. ......................................................................................................... 82
5-2 Distribution of sample points representing the location of Asian Citrus Psyllid monitoring points. ............................................................................................... 83
5-3 Developed shaking machine used for collecting Asian citrus psyllid in the field. .................................................................................................................... 84
5-4 Yellow sticky trap used for capturing Asian citrus psyllid. ................................... 85
5-5 Prediction map of Asian citrus psyllids using inverse distance weighting technique. ........................................................................................................... 89
5-6 Prediction map of Asian citrus psyllids using ordinary kriging technique. ........... 89
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5-7 Prediction map of Asian citrus psyllids using simple kriging technique. .............. 90
5-8 The cross validation comparison of the ACP distribution map between inverse distance weighting and simple kriging method. ...................................... 91
5-9 The cross validation comparison of the ACP distribution map between inverse weighting distance and ordinary kriging. ................................................ 92
5-10 The percent error with different interpolation methods for ACP prediction. ........ 92
6-1 Comparison of two different methods on the total time required to monitor Asian citrus psyllid in citrus groves. .................................................................... 97
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LIST OF ABBRIVATION
ACP
APHIS
CREC
ESRI
FASS
HLB
GPS
Asian Citrus Psyllid
Animal and Plant Health
Citrus Research and Education Center
Environmental Systems Research Institute
Florida Agricultural Statistics Service
Huanglongbing
Global Positioning System
GLM General Linear Model
IDW Inverse Distance Weighting
NASS National Agricultural Statistics Service
OK Ordinary Kriging
SK Simple kriging
PE Percent Error
RMSE Root-Mean-Squared –Error
RI Relative Improvement
USDA United States Department of Agriculture
USB Universal Serial Bus
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
MONITORING AND MAPPING ASIAN CITRUS PSYLLID USING SHAKING MACHINE
By
Muna Jamil Abbas
May 2017 Chair: Reza Ehsani Major: Agricultural and Biological Engineering
Asian citrus psyllid (ACP, Diaphorina citri Kuwayama) is a major pest of citrus in
Florida. ACPs are of particular importance because they spread citrus greening disease
or citrus Huanglongbing (HLB). HLB is one of the most serious problems affecting
Florida’s citrus industry due to rapid transmission of the disease within groves and
corresponding rapid decline in productivity of infected trees. The existing scouting
methods of manual insect pest sampling is time consuming, labor intensive and costly.
The accurate and rapid identification as well as ACP monitoring is highly recommended.
Therefore, the main goal of this research was to develop a machine for quick and
cost - effective collection of psyllids in citrus groves that simulates the tap sampling
method. A shaking machine was built and fabricated at the Citrus Research and
Education Center, Lake Alfred, FL. First, sampling techniques were tested in terms of
optimal placement (horizontal and vertical) of colored sticky traps that monitor ACP
density distribution using different sampling patterns (grid and zigzag). From the ACP
distribution information, geo-insect’s prediction maps were generated. The results
showed that there were non-significant differences between the trap positions at 0.05
level of significance. The spatial distribution for ACP created using three different
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interpolation methods: inverse distance weighting (IDW), ordinary kriging (OK), and
simple kriging (SK).
In order to define the shaking machine operation parameters, a second field test
was performed in the citrus groves. In this test, two different experiments were
performed. The first experiment was the tap sampling experiment. In this experiment a
modified version of the tap sampling method of Stansly et al., 2010 was performed, with
the exception that no insects were collected. The shaking duration (5, 10, and 15 s) and
the limb length (0.889, 0990, and 1.143 m) were selected as two important parameters
by which to define the acceleration exerted on the limb using the tap method. The
results show that there were no significant differences among the shaking duration and
limb length at 0.05 significance level. The second experiment was the shaking machine
experiment in which the shaking frequencies (0.26, 2.33, and 4.00 Hz) and shaking
durations (5, 10, and 15 sec) are two important parameters to define the acceleration
exerted on the limb using the shaking machine. The results showed that there was a
significant difference among the shaking frequencies and shaking duration with 0.05
significance level.
For monitoring and mapping the ACP, a fourth field test was performed in citrus
groves that simulate tap sampling method. In this field test, a shaking machine was
used to collect the psyllids in defined locations to follow the zigzag pattern. The ultimate
goal for this study is to utilize the shaking machine to monitor the ACP in citrus groves
and evaluate the accuracies of the generated maps under different interpolation
methods.
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CHAPTER 1 INTRODUCTION
The State of Florida accounted for 56% of the citrus production total of the United
States and California is 41% and 3% for Arizona and Texas (USDA, 2016).This makes
Florida the second largest producer of citrus in the world behind Brazil (Hodges and
Spreen, 2012).
Citrus could be attacked by various pests such as insects, mites, nematodes
which effect on the quality and quantity of the citrus yield. Plants can be attacked by
wide varieties of insects which can cause physical damages or carry a pathogen from
infected to healthy plants (Meyer, 2007). Asian citrus psyllid Diaphorina Citri Kuwayama
started in Asia, India and in the United States, it was first observed in Florida and Texas
in 1998 and 2001 respectively (Grafton-Cardwell et al., 2006). Asian citrus psyllid is of
particular importance because they spread citrus greening disease or Huanglongbing
(Pelz-Stelinski et al., 2010). Huanglongbing (HLB) is one of the most serious problems
in Florida’s citrus due to rapid transmission of the disease within groves and
corresponding rapid decline in productivity of infected trees (Mishra et al., 2012; Quarles
2013). Asian citrus psyllids are of particular importance because represents the vector
for spreading citrus greening disease or citrus Huanglongbing (HLB) (Pelz-Stelinski et
al., 2010). The most common symptoms for HLB disease is the small and misshapen
fruit with bitter taste (Hodge and Spreen, 2012). Further symptoms which shown in the
infected tree is the yellow shoots on the tree (Gomez, 2009).
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Figure 1-1. Citrus greening (HLB) disease symptoms: A represents the yellow shoots on the affected tree; B represents the misshapen fruit on the affected tree. [Adapted from: Gomez, 2009].
Presence of new flush and temperature are the two main factors that affect
psyllid reproduction (Rogers and Stansly, 2006). In addition, 20-30°C is the ideal
temperature conditions for the psyllids, while temperature above 32.22 °C cause
populations to declined (Rogers and Stansly, 2006). Majumdar and Fadamiro (2009)
highlighted the importance of early detection of ACP due to their ability to reproduction
under certain conditions. High populations of ACP can cause direct plant damage
(Halbert and Manjunath, 2004). In order to control the pests, an efficient scouting
method that tracks the pest population over time is required (Stansly et al., 2010). Adult
Asian Citrus Psyllids monitored by using one of the following methods: Tap sampling,
sticky traps, and sweep nets, while the nymphs and eggs of ACP can be detected by
direct observation (Stansly et al., 2010). Stem-tap sampling can be used to monitor the
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adults of Asian citrus psyllids (Hall et al., 2007, Hall and Hentz 2010, and Stansly et al.,
2010). Tap sampling method represented by placing a surface below the branch of the
tree such as a clipboard and hit the branch with a PVC pipe three times and the fallen
adults of Psyllids will be counted and recorded (Stansly et al., 2010).
Problem Statement
One of the most common pests for citrus in Florida are Asian citrus psyllid.
Insects cause two kinds of damage to growing crops: (1) direct physical damage by
eating leaves, fruit and/or roots and (2) indirect biological damage by transmission plant
pathogens (Halbert and Manjunath, 2004). Asian citrus psyllid is of particular
importance because they spread citrus greening disease or Huanglongbing (Pelz-
Stelinski et al., 2010). HLB is one of the most serious problems in Florida’s citrus
industry and worldwide due to its rapid transmission from infected to healthy trees within
and between groves and as a consequences rapid decline in the productivity of the
grove (Mishra et al., 2012; Quarles 2013). In order to control the pests, an efficient
scouting method that tracks the pest population over time is required (Stansly et al.,
2010). Currently the exist methods of insect pest monitoring is time consuming and
labor intensive and costly.
Specific Objectives
The long-term objective of this study is to develop a system for quantifying and
mapping ACP in the citrus groves. The specific objectives of this study are to:
1. To develop a machine for quick and cost effective collection and monitoring of Asian citrus psyllid in citrus groves.
2. To conduct field trials to determine the optimal parameters of the shaking machine.
3. To generate geo-referenced Asian citrus psyllid prediction map.
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CHAPTER 2 LITRATURE REVEIW
Identification of Asian Citrus Psyllid (ACP)
Asian citrus psyllid Diaphorina Citri Kuwayama started was first observed in
Florida and Texas in 1998 and 2001 respectively (Grafton-Cardwell et al., 2006). Asian
citrus psyllids are of particular importance because represents one of the vectors for
spreading citrus greening disease or citrus Huanglongbing (HLB) (Pelz-Stelinski et al.,
2010). The Asian citrus psyllid (ACP) is a tiny mottled brown insect, 3-4 mm in length
with three development stages, from egg through five nymph instars to the adult stage
(Mead and Fasulo, 1998). The psyllid female can lay eggs up to 857 on grapefruit plant
and 572 eggs on rough lemon plant, and it need 16-17 days to reach the adult stage
under 25˚ C (Grafton-Cardwell et al., 2006). The nymphs ranged from 1/100-1/14 inch in
length, yellowish – orange in color (Grafton-Cardwell and Daugherty, 2013). Figure (2-1)
shows the Asian citrus psyllid and their three development stages.
Figure 2-1. Asian Citrus Psyllid and its three different stages: A represents the eggs
stage; B represents the nymphal stage, and C represents the adult stage: [Adapted from: Mead and Fasulo, 1998].
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Ecology of Asian Citrus Psyllid
There are three factors affects the development of psyllids population including:
temperature, relative humidity and rainfall (Teck et al., 2011). Rogers and Stansly
(2006) found that the new flush and the temperature effects on the psyllids population.
They found that the adults of psyllids found on the new flush and the female psyllid lay
the eggs on the young leaves. Furthermore, they found that in the absence of the new
flush, the psyllids, the psyllids are shown on the underside of the leaves. Also, they
found that the ideal temperature for female psyllids is 20-30 ˚C.
Martini et al. (2016) studied the factors affecting the abundance of Asian Citrus
psyllid during winter season in Florida citrus. They surveyed the population at three
heights of the canopy and at four cardinal directions. Their results showed the number
of psyllid was more with canopies facing south than the canopies facing north. Also they
found there are two other factors correlated positively with the psyllid population
including the relative humidity and the emergence of new leaves.
Gutierrez and Ponti (2013) found that the psyllid reproduction occurs in spring
time when the leaf flushes and also they found that the flushing occur after the
harvesting and fruit maturation. However, they found that psyllid reproduction is less
with foliage mature.
Tsai et al. (2002) studied the seasonal abundance of Asian citrus psyllid in
Southern Florida and they found that the highest population was in May, August and
from October through December. In the spring and with very humid conditions, the
nymphs’ development stage is high (Aubert, 1990). Liu and Tsai (2000) found a
significant effects of the temperature ranged from 25-28˚C on the development rate and
the reproduction of Asian citrus psyllid.
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Teck et al. (2011) studied the seasonal population dynamics of the Asian citrus
psyllid. They found that the population peaks for the adult and eggs are in August-
September, February-March and June-July. Also, they found that their population are
correlated with the new flushes and negatively correlated with the temperature and
relative humidity.
Rakhshani and Saeedufar (2011) found that Asian citrus psyllid population
increase with the moderate climate and with new flushes.
Setamou and Bartels (2015) reported edges effects of the field on the Asian
Citrus Psyllid distribution. They found that the edges have a strong effect on the psyllid
distribution. Their results showed the number of psyllids are more on the perimeter
trees. Also, they found that a higher number for psyllids on the trees located on the east
and south sides than those on other sides of the groves. Their study showed also psyllid
density declined significantly with increasing the distance from the edge to the center of
the grove. Furthermore, they found that the field edge with non-surrounding area hold
more psyllids than edges surrounded by other groves.
Monitoring Methods for Asian Citrus Psyllid
The main objective of sampling the insect pest is to detect their population and
their distribution (Zehnder, 2014). Field conditions such as field size, crop and the field
layout size are parameters determine the way of sampling (Zehnder, 2014). When the
field is a squared-shape, ʻʻU’’ sampling pattern is used, while ʻʻW’’ sampling pattern is
used for a long and narrow field (Zehnder, 2014). Majumdar and Fadamiro (2009)
indicated to the importance of early detection of Asian citrus psyllid due to their ability to
reproduction under certain conditions. High populations of Asian citrus psyllids can
cause direct plant damage (Halbert and manjunath, 2004). In order to control the pests,
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an efficient scouting method that track the pest population over the time is required
(Stansly et al., 2010).
Visual Sampling
Visual survey method is used for the new flush to detect the psyllid and the
monitoring procedure should conduct on all the development stages of insect (Crafton-
Cardwell, 2006). Visual survey is used for detect psyllid nymphs and eggs (Stansly et al.,
2010). The method procedure consists of sampling of 10 trees on each of the north, east, south,
west, and the center of the orchard and select a young leaf and examine the flush of that tree
for exiting all the psyllid stages (Grafton-Cardwell, 2016).
Figure 2-2. Visual inspecting for the new flush [Adapted from: Grafton-Cardwell et al.,2006]
Tap Sampling
Tap sampling is a sampling method for psyllid adult and nymphs (Quarles, 2013).
Stem-tap sampling is one of the effective sampling methods for detect Asian citrus
psyllid which started on 2006 (Stansly et al., 2010). In this method two materials should
be used to complete the procedure of this method in four steps, and this material are
white clipboard, and PVC pipe for beating the branches (Stansly et al., 2010). The
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procedure for this method is to place the clipboard beneath the branch and strike the
branch with the pipe three times and then the number of the falling adults on the
clipboard will be counted and recorded (Stansly et al., 2010). The pros of this method is
the low cost and this method is an effective method with high psyllid population, while
the cons of this method is cannot be used for the small trees (USDA /APHIS, 2010).
Figure 2-3. Use of clipboard (left) and number of psyllids on white clipboard (right). [Adapted from: Stansly et al.,2010]
Hall and Hentz (2010) used two different sampling methods: stem-tap and yellow
sticky traps in order to estimate number of adult Diaphorina citri Kuwayama (Hemiptera:
Psyllide). They found that both sampling methods were effective for detecting adults’
psyllids in trees. However, they found that sticky traps sampling is better when adults’
densities are low. Besides, they found that sticky traps can be used for trees with height
less than a meter. Whereas, tap sampling is not accurate method when the number of
adults is large.
Sticky Traps
A yellow or yellow-green sticky cards method is one of the traditional and
effective method for flying adult of Asian citrus psyllid detection (Stansly et al., 2010).
The drawback of this method is the labor cost since the cost for each trap is around $1
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per trap and the labor cost for hung and collect and read for each single trap (Stansly et
al., 2010). The pros of this method are the time required for monitoring the groves using
the sticky traps is less and the method is efficient for early detection, while the cons of
this method is the cost for each trap is approximately $1 and required a technical people
that can read the sticky traps and identify the psyllids (USDA /APHIS, 2010).
Figure 2-4. Use the sticky traps (left) and number of Asian citrus psyllids on the traps (right).). [Adapted from: Stansly et al., 2010]
Atakan and Canhilal (2004) found the number of insects are negatively affected
by the traps height, they reported that the number of whiteflies were captured at 60 cm
were more than 80,100, and 120cm. Gencsoylu (2007) conducted a study on using
yellow sticky cards to monitor some cotton pests at different heights, positions,
dimensions and directions during 2004 and 2005 .He found that the largest population
was captured at 25 cm for Franklineiella spp while for Bemisia and Empoasca spp , the
largest was at height 30 cm above the ground level. For sticky cards position, he found
that vertical position has more effective to capture Empoasca and Frankliniella spp.
whereas for B. tabaci there was no significant differences between the two positions.
Hall et al. (2010) compared six types of sticky cards traps with different colors to
evaluate them for monitoring Asian citrus psyllid in citrus in Florida and Texas. They
26
found a positive correlation between the number of adults captured on traps and the
percent reflectance in the yellow region, whereas, the reflectance in the blue region
there was a negative correlation with number of adults captured on traps. Yen et al.,
(2013) evaluated five sampling techniques for detecting of the tomato potato psyllid,
bactericera cockerelli (sulc) (Hemiptera: psylloidea: Triozidae) in potato crop on the
north island of New Zealand. The five sampling methods was sticky traps, water traps,
sweep netting, vacuum sampling and direct searching). Their results showed that sticky
traps and water traps outperform than other methods for detecting psyllids. However,
there was a minor difference between sticky traps and water traps due to the sampling
variability. Their results showed that the mean parameter estimates for sticky traps and
water traps were positive, whereas was negative for other methods. Premalatha and
Rajangam (2011) investigated the efficacy of yellow sticky traps and yellow charts
coated with castor oil against greenhouse whitefly, Trialeurodes vaporarioum in
gerbera. On the third, fifth, seventh and fifteenth days after initial installation of traps,
they observed the whitefly number on sticky cards and yellow charts. They found that
the yellow charts coated with castor oil coat attracted 220 whitefly adults, whereas 19
adults for yellow sticky cards on the third day in the same variety (Cassiana). Mensah
(1996) conducted three experiments from 1992 to 1994 for monitoring population of
Austroasca Viridigrisea (Paoli) using colored sticky traps. Traps were placed at a height
of either 25, 50, 75, 100, 125 or 150 cm from the ground. The preference test showed
that yellow traps has a strong caught of A. Viridigrisea adults which is 8-12 per trap per
day. Whereas, the lowest number of adults captured were by green, red, deep blue,
black, magenta, and true blue respectively.
27
Sweep Nets
Sampling with this method is based on using a sweep net for capturing and
recording the adult of psyllid. Sweep net is a 15-inches with 6-10 inches tall. The sweep
net procedure is based on swing the sweep net in an 180˚ arc, from right to left, and one
step walk, then sweep from left to right with same arc. The drawback of this method is
the possibility to spread the disease in the grove in case of the disease presence
(Stansly et al., 2010). The pros of this method is the possibility for detection all the life
stages including: eggs, nymphs and adults, while the cons of this method is the time
required for detection and requiring training people (USDA /APHIS, 2010).
Figure 2-5. Using the sweep net for psyllid detection. [Adapted from: Stansly et al., 2010]
Monoz et al. (2015) evaluated and compared five different sampling methods for
detection and monitoring of the Asian Citrus Psyllid including: sticky traps, suction
sampling, sweep net sampling, and visual sampling in citrus groves under different ACP
insecticide management programs. For visual sampling method, it took 2.2 times than a
stem-tap sample. At 0.1 adults per tap density, stem-tap sampling is not the most
reliable method to reach 0.25 “standard error to mean”. They recommended the random
steps than random selection for trees to detect ACP density and other pest population
for cost-effective way. The most sensitive method was the suction sampling while
28
sweep net has similar sensitivity to stem-tap sampling. However, it was time consuming.
ACP detection was more with visual sampling than stem-tap sampling at densities
below 0.013 adult per tap. With moderate to high ACP densities, stem-taps were the
more precise method. Their conclusion is that other sampling methods were more
efficient at low ACP densities than stem – tap samples to achieve 25% precision.
However, stem-taps method can be used when the threshold of 0.1% tap or above.
D Vacuum
This method depends on using a backpack vacuum to suck the insect pest from the
foliage (USDA /APHIS, 2010). The pros of this method is working better with young and
small trees and cons of this method is not efficient with large area and trees, requiring
two people for working and time consuming and labor costly in order to separate the
debris from target insect (USDA /APHIS, 2010).
Suction Traps
This method is based on using vacuum attached to two different heights tubes and the
tube attached to the collection jar (USDA /APHIS, 2010). The pros of this method is can
be used for monitoring psyllids over time and space, while the cons of this method is the
high initial cost to build the traps and the method is not efficient for monitoring the psyllid
with low population (USDA /APHIS, 2010).
Models Proposed to Predict Insect Variation over an Area
Leaf Washing Method
Martini et al. (2012) evaluated sampling method called leaf washing method to
collect and count nymphs of potato psyllids. The main parts of this system are the
vacuum pump, Buchner, carboy, water reservoir, spigot, five polypropylene Buchner
funnels, and pipes. The leaf washing process is started with immersing all samples of
29
infested leaves in cold water to remove dust and sand and then immersing them in hot
water (>85 °C) for five seconds. Later, extracting nymphs from hot water using vacuum
pump to force water goes through fine mesh organza fabric. Then, when removing
organza fabric, they counted psyllids nymphs under a stereoscope. Their results show a
negative correlation between extraction time and water temperature. From their results,
they recommended to use the leaves from the mid portion of the canopy.
Figure 2-6. Leaf washing method filtering device. [Adapted from: Martini et al., 2012].
Shake - and – Washing Technique
Zacharda et al. (1988) developed a shake-and-wash technique for monitoring
mites in apple orchards. This technique depends on removing mites from the plant
materials by adding 300-500 mL of 80-90% ethanol in the jar which containing about 10-
15 leaves, spurs, or shoots with undeveloped leaves and then shake them for 5-10 s.
The shaken is repeated for one more time after a rest of 1 min later, they removed plant
materials using forceps. After mites being settled, the recovered mites counted. Their
results showed that this technique is more efficient by 10-20% than direct count method.
30
Two Simple Insect Sampling Devices
Nishida and Takara (1979) used two different devices for insect sampling. The
first device is the dry shaking device which separated insects from plant materials
through shaken the entire device 10-15 times up and down in vertical direction. Then
the insects will be dropped into the vial at the bottom. The wet shaking device utilized a
washing agent to separate insects from plant materials. The plant sample is placed in
the top of the container and 30% of ethyl alcohol is poured into the container and let the
device shaken up and down in the vertical direction 10-15 times. Later the liquid flow
through the nylon organdy sieve to catch the insects while the washing agent drains to
the container.
Washing Machine for Arthropods Recovery
Leigh et al. (1984) developed a washing machine for Arthropods recovery from
plant leaves. The machines components are: washing tank, solution inflow jets, flushing
jet flow, rate control valves, outflow pipe, sieves, pump. They collected 25 cotton leaves
randomly and put them in that tank for 10 minutes then after that, the leaves were
removed by forceps. The floating spider mite recovered using the screens. The
recovered spider mites in different stages were counted. Besides, they flushed the
washing solution remaining from the washing tank through a sieves for spider recovery.
In order to save the counting time especially when large number of spider mite are
collected. They developed a subsample system which consists of a centimeter grid. The
benefits of this system is that the pump ca be used to supply two or three washing
machines at the same time. Their results showed that with operating two washing
machines at the same time, they can complete five to six samples per hour. Their
31
results showed a high correlation for the spider mites numbers (r=0.92) calculated from
subsamples compared to the total sample counts.
Figure 2-7. Leaf washing machine for Arthropods recovery from plant leaves. [Adapted: from Leigh et al.,1984.]
Three Different Vacuum Devices for ACP Detection
Thomas D.B. (2012) compared three different vacuum devices for Asian citrus
psyllids detection. The first device is a vacuum with AC rechargeable handled while the
second vacuum was a DC model power handled. Two devices were in the same size
and weight and nozzle diameter (32mm). The third device was a leaf blower with
modification for the blower tube to the intake port. They conducted their test in the
Valencia orange groves for Psyllids detection. The basic method used is that the leaves
and the shoots was brushed using the intake nozzles of the vacuum for 5 minutes. They
sampled different area each week, and they finish the sampling within 6 months. Their
results showed that the leaf blower cached more psyllids other devices. However, the
mean differences for the all three devices (AC, DC, and leaf blower devices) were
statistically significant at: 17.4,33, and 96.8 respectively.
32
Figure 2-8. Vacuum devices used for detect Asian citrus psyllid. [Adapted from: Thomas D.B. 2012].
An Automated System for Detection and Extraction Pest in Paddy Field
Miranda et al. (2014) established an automated system for detection and
extraction pest in paddy field. They used a wireless camera with a sticky trap for insect
pests’ detection. Simple and efficient image processing mechanism was used for insect
pests’ detection which based on five different steps including: image acquisition, image
pre-processing, detection of pests in the image, filtering of the image, and extraction of
the detected pests. Their results showed that the system was simple and efficient to
detect the insects in the captured image.
Figure 2-9. Global architectural design. [Adapted from: Miranda et al., 2014].
33
An Autonomous System for Insect Pest Detection
Thangalak and Ramanujan (2015) designed an autonomous system for insect
pests’ detection in three different fields. The system consists of electronic trap with three
different layers and thickness to catch different insects, and the trap covered by the bait
to attract the insects. Also, the system consists of a large plastic bucket with lid, three
different types of mesh, vans, plastic funnel, U-V light to attract the insects, and IR
cameras to capture the image of the trapped insects. The captured image sent remotely
for insect density estimation. Therefore, this information can help the farmer when and
where to use the pesticides.
Figure 2-10. Trap design. [Adapted from: Thangalak and Ramanujan, 2015].
Shaker Machine for Different Applications
Mobile Limb Shaker
Kemp and Melanson (1997) designed a mobile limb shaker for apple harvesting.
The machine consisted of a shaker boom and a hydraulic cylinder to allow the shaker
boom to swing in a horizontal plane. Also it consists of a boom holder, a roller chain,
and hydraulic motor with sprockets. Three different shaking mechanisms were designed
and evaluated including: a double-crank unit (straight line), a single-crank unit built into
the clamp housing (single crank-pivot) and a single –crank unit mounted on the opposite
34
end of the boom (single crank-straight line). Their results showed that the single- crank
unit was the best when considering the effectiveness of fruit removal and mechanical
design. Also, the shaker rate was 20-53 trees/h with more than 90% of fruits removal.
Figure 2-11. Mobile limb shaker for apple harvester. [Adapted from: Kemp and
Melanson ,1997].
Trunk Shaker
Leone et al. (2015) used a trunk shaker to determine the frequency, acceleration
and shaking time on the removal percentage of olives. Their results showed that fruit
removal percentage is positively correlated with frequency and acceleration for all olive
cultivars. The optimal frequencies that maximizes the fruit removal percentage were 25
Hz, 23 Hz and 27 Hz for Frantoio and Picholine, for Leccino and for Cima di cultivars
respectively. The acceleration measured on the trunk was (70.41-99.25 m 𝑆−2) in
range. The optimal shaking time ranged from 6-8 s. They found that increasing the
shaking time beyond the optimal value will not result in increasing the fruit removal
percentage and will effects on the equipment life.
35
Figure 2-12. Olive trunk shaker [Adapted from: Leone et al.,2015].
Vertical Canopy Shaker
Sumner (1973) investigated the effects of the frequency and stroke of the vertical
canopy shaker on the removal of Valencia oranges using two shaker drive systems. The
first drive system was a 112⁄ in –dia recycling hydraulic cylinder and the second drive
system was a hydraulic-driven crank. The frequency used in the first drive system is up
to (225 cpm) while the shaking frequency in the second drive system was up to (350
cpm) with stroke adjustments from 4-12 in. The first test was conducted in 1970 using
the two drive systems (without flywheel). Selected limbs were shaken for 18 sec with
range of (6,9 and 12 in) of stroke displacement. The results of the first test showed that
percentage of the fruit removal was higher with using the crank-driver- shaker than the
cylinder-drive-shaker with low- frequency range. The second test was in 1971 using the
crank- drive – shaker with adding flywheel. The shaker strokes were (6,9 and 12 in) and
the selected limbs was shaken for 10 sec. The results of the second test showed that
the percentage of the fruit removal was reduced. They concluded from their study that
the fruit removal was proportional to the shaking frequency and stroke in 1970 test.
Their conclusion from the test in 1971 (adding the flywheel to the shaker) was that the
influence of the stroke and the shaking frequency is less on the selectivity ratio.
36
Figure 2-13. Shaker unit with crank drive system before adding flywheel. [Adapted from:
Sumner, H.R.1973].
37
CHAPTER 3 EVALUATION OF SAMPLING PATTERNS METHODS TO DETECT AND MONITOR
ASIAN CITRUS PSYLLID IN CITRUS GROVES
Understanding where the insect pest population high is very important for
managing their control across the field and the objectives of insect sampling is to detect
their densities and distribution in the field (Zehnder, 2014).
Identify, locate and determine the infestation severity of pest are the primary
goals of pest monitoring (Schnelle and Rebek, 2016). The crop, field size determines
the sampling procedure, for instance when the field is a square shape then the “U”
sampling pattern is more commonly used, while when the field is long and narrow then
the “Zig-Zag” or” Z” sampling pattern is more appropriate (Zehnder,2014). Sampling
techniques used for insect pest vary depends on the type of insect. Visual sampling,
sweep net, sticky traps, and beating sampling are methods used for sampling Asian
Citrus Psyllid.
Adults of Asian citrus psyllid can be monitored using yellow sticky traps (Hall et
al., 2007). Sticky traps used for Asian citrus psyllid detection provide an evidence of
infestation (Majumdar and Fadamiro,2009).
The main objectives of this study are: Developing sampling techniques in terms
of optimal position (Horizontal, and Vertical) for placing colored sticky traps to monitor
Asian citrus psyllid using different pattern sampling (grid and zigzag patterns), and
generating geo-referenced prediction maps under citrus groves in Florida using three
different interpolation method including: Inverse weighting distance, ordinary kriging
method and simple kriging method.
38
Materials and Methods
The Study Area
Experimental test was performed in organic citrus groves located in winter
Garden, Florida at 28 30' 56.49" N 81 40' 10.49" W on April and May of 2015. The area
size used for the field experiment was 1.19 ha (2.94 ac) as shown in Figure 3-1 from the
total area size of 3.84 ha (9.50 ac).
Figure 3-1. Study area.
Insect Sampling Procedures:
In order to evaluate the differences of commonly used methods for sampling
patterns for insect pests (Asian citrus psyllid), different sampling techniques
implemented to reflect the population density. Yellow sticky traps were used to monitor
adult ACP (Stansly et al., 2010). Two different patterns of sticky traps distribution used
to monitor psyllids population. These patterns include: Regular grid pattern and the
39
regular zigzag pattern as shown in the Figure 3-2. Special sticky trapes placed at
specific location in the field to detect their activities. Two trapes placed at each tree with
two different positions (horizontal and vertical) as shown in Figure 3-3 to trap the pests
in citrus orchard to see which placement is more effective to catch insects. Traps set
approximately at the middle height of the tree. The traps were aligned in a south to
north direction. Insect sampling, sample locations georeferenced, then by looking at the
insect count results, it is possible to correlate the insect population with spatial data. By
using Real time kinetic (RTK) GPS system, the location of each Ground Control Points
(GPS) is defined on a topographic map. The number of adult psyllid captured on each
sticky traps are counted manually and then analyzed. The interpolation process was
applied to the datasets using three different interpolation methods including: Inverse
Distance Weighting (IDW), ordinary kriging (OK) and simple kriging (SK) to estimate
unknown ACP population in the neighborhood. Geostatistical analyses were employed
using the geostatistical analyst extension in ArcMap (ArcGIS 10.3.1 © 1999-2015 Esri
Inc.
IDW interpolation estimates the unknown values with a mathematical formula
from the nearby knowing values as shown in equation 3-1and 3-2 (Yasrebi et al., 2009):
Z(x)=∑ 𝑊𝑖 𝑍𝑖
∑ 𝑊𝑖
(3-1)
𝑊𝑖 = 1
𝑑𝑖𝑝 (3-2)
40
Where: Z: is the unknown value, and 𝑊𝑖: is the measured value, and 𝑑 : is the distance
between the sample point and the unknown point.
P: power parameter.
Equation 3-3 (Yasrebi et al., 2009) used to estimate the predicted value using
ordinary kriging method.
Z(x)= ∑ 𝜆𝑖 𝑍𝑖𝑁𝑖=1 (3-3)
Where:
Z(x): The predicted value, and Zi: The measured value at location I, with 𝛌: unknown
weight for the measured value at the i location, and N: The number of measured values.
The measure of the accuracy, called the Root-mean -squared error (RMSE)
(Karydas et al.,2009) is the measurement will be used for testing the prediction map.
Equation 3-4 shows the mathematical formula used for RMSE estimation (Odeh et al.,
1994).
RMSE = [ 1
𝑛 ∑ (|𝑧(𝑥𝑖 ) − ��𝑛
𝑖=1 (𝑥𝑖 )|)2]1
2⁄ (3-4)
Where:
RMSE: Root-mean –Squared-Error.
Z (Xi): the observed value at location I.
��(Xi): the predicted value at location I.
N: the sample size
41
Cross validation can be used to evaluate the results of interpolation techniques
using different sampling methods. Finally, the relative improvement (RI) of the best
method can be calculated using Equation 3-5 (Yasrebi et al., 2009).
RI = 100 |𝑅𝑀𝑆𝐸𝑏𝑒𝑠𝑡−𝑅𝑀𝑆𝐸𝑐𝑢𝑟𝑟𝑒𝑛𝑡|
𝑅𝑀𝑆𝐸𝑏𝑒𝑠𝑡
(3-5)
Where:
RI: Relative improvement
𝑅𝑀𝑆𝐸𝑏𝑒𝑠𝑡 : The minimum value of RMSE
𝑅𝑀𝑆𝐸𝑐𝑢𝑟𝑟𝑒𝑛𝑡: The RMSE of the current model
The state plane projection and North American Datum 1983(NAD 83) specified
the location of the study area in metric units. The results of the interpolation were
represented over the study site map which shown in the next section.
Figure 3-2. Two different patterns of sticky traps distribution used to monitor psyllids population. A) represents grid sampling pattern and B) represents zigzag sampling pattern.
42
Figure 3-3. Two positions for traps' placement.
Figure 3-4. Distribution of sample points representing the location of Asian citrus psyllid monitoring reference points.
In the Figure 3-4 each symbol in the point layer represents a location where the
ACP has been measured. 18 tree locations were randomly selected in the field to take
the measurements and represented as reference points which used for maps accuracy
assessment. The general procedure for collecting the true ground truth for ACP was
placing one trap on each selected tree and leave it for one week. Traps set
approximately at the middle height of the tree. The traps were aligned in a south to
north direction. The number of adult psyllid captured on each sticky traps are counted
manually.
43
Results and Discussion
Statistical Analysis
In terms of data analysis, to analyze the results of the experiment using IBM
SPSS statistics version 23 (IBM Corp. copy right IBM corporation 1898, 2015). A
compare mean / one –way ANOVA was used to determine significant differences at P =
0.05. The results of the analysis for selected sticky traps at different positions
(horizontal and vertical) are shown in Table 3-1.
Table 3-1. Analysis of variance of data on the number of psyllids captured at different trap position (horizontal and vertical).
Source Sum of squares df Mean square F Significance
Sticky trap position 9.481 1 9.481 0.447 0.505 Error 2249.185 106 21.219 Total 2258.667 107
** Significant at P< 0.05
Table 3-1 shows that the effect of two trap positions in capturing ACP was not
statistically significant as determined by one-way ANOVA (F (1,107) = 0.447, P= 0.505.
Figure 3-5 shows mean number of ACP captured using two different traps positions.
The orange boxplot represents horizontal traps position, while the blue boxplot
represents vertical traps position. It can be seen from the mean for both datasets are
approximately around (5). However, there is a little more variation in vertical position
which approximately ranges from o to 16, whereas horizontal position ranges
approximately from 0 to 15.
44
Figure 3-5. The plot of number of psyllids captured by different traps position: traps
positions is represented as: orange box plot, horizontal position; blue box plot, vertical position.
Exploratory Statistics for Asian Citrus Psyllid under Different Sampling Patterns with Different Traps Position
The summary statistics of ACP distribution using different sampling methods with
different traps position are shown in Table 3-2. The positive value of skewness and
kurtosis values for the grid sampling method are (0.802-1.001) and (2.814-4.398)
respectively. While under zigzag pattern sampling, the skewness values and kurtosis
values are (1.276-2.33) and (4.212-9.807) respectively.
Table 3-2. Descriptive statistics for ACP distribution using different sampling methods Sampling method
Traps position
Min
Max
Mean
Std.dev Skewness
Kurtosis
Median
grid pattern horizontal 0 18 6.087
4.055 1.001 4.398 5
grid pattern vertical 2 17 7 4.338 0.802 2.814 7 zigzag pattern Horizontal 2 17 6.19
3 3.936 1.276 4.212 5
zigzag pattern vertical 0 30 6.548
5.789 2.33 9.807 6
The prediction error mean and the root- mean- squared prediction error can be
seen in the Figure 3-6 and Figure 3-7 for all sampling patterns with different traps
positions using three different interpolation methods .Figure 3-6 shows that the
45
prediction error using IDW, Ordinary kriging and simple kriging is -0.135,-0.098 and -
0.025 respectively whearas for RMSR is 4.80,4.92 and 4.656 respectively for grid
pattern . While for the zigzag pattern, the prediction error using IDW, Ordinary kriging
and simple kriging is -0.416,-0.457 and -0.377 respectively and for RMSR is
4.029,3.968 and 3.870 respectively and as shown in the Table 3-3. The reason behind
why we get different results with different sampling patterns which resulted in different
prediction maps is the interpolation techniques used are different methods .
Furthermore, the denisty and the spacing of the sample points will effects on the results
and then on the accuracy of the prediction map. It can be seen that the density and the
spacing of the samples using grid sampling is totally different from using the zigzag
pattern, in turn this will effects on the prediction maps outputs .
Table 3-3. The means of mean (M) and root mean square error (RMSE) for different traps placement under two different sampling patterns.
Sampling pattern
Interpolation method Prediction error
M RMSE
grid pattern Inverse weighting distance
-0.135 4.80
Ordinary kriging -0.098 4.90
Simple kriging -0.025 4.656
zigzag pattern Inverse weighting distance
-0.416 4.029
Ordinary kriging -0.457 3.968
Simple kriging -0.377 3.870
46
Figure 3-6. The cross validation comparison of the ACP distribution map by different interpolation methods using grid pattern.
Figure 3-7. The cross validation comparison of the ACP distribution map by different
interpolation methods using zigzag pattern.
Comparison of the Interpolated Maps by the Two Sampling Patterns and Two Different Traps Position
The spatial distribution of Asian Citrus Psyllid is shown in the Figures (3-8,3-9,3-
10 ,3-11,3-12 and 3-13) using grid sampling pattern with two different traps position.
While Figures (3-14,3-15,3-16 ,3-17,3-18 and 3-19) using zigzag sampling pattern with
two different traps position which is due to many factors because the insect distribution
affected by the insects’ development stage, the season, the weather conditions. Each
prediction map provide insect population distribution represented by a specific color on
47
the map which is shown in the accompanying key. Figures 3-8,3-9, and 3-10 show the
distribution map for Asian Citrus Psyllid using sticky traps distributed in grid pattern
under horizontal position. The image shows that the large number of psyllid caught by
horizontal position of traps in the west and north side of the field which confirm the edge
effects fact. While the Figures 3-11,3-12, and 3-13 show the distribution map for Asian
Citrus Psyllid using sticky traps distributed in grid pattern under vertical position. The
image shows the large number of psyllids caught by the vertical position of the traps are
shown in the middle of the field. Figures 3-14, 3-15, and 3-16 shows the distribution
map for ACP using sticky traps distributed in zigzag pattern with the horizontal traps
position. The image shows that the high density of psyllid is more distributed in the
western north edge of the field and also in the center. In contrast, it can be seen that
there is different trend of psyllids distribution in the field using zigzag pattern using
sticky traps in vertical position as shown in Figures.3-17,3-18, and 3-19. The images
show that the ACP is more distributed in the north of the field. The possible
explanations for the high infestation with adult psyllids in certain areas of the field is the
new flush and the temperature which effects on the psyllids population (Rogers and
Stansly ,2006) and psyllid population correlated positively with the relative humidity and
the emergence of new leaves (Martini et al.,2016). Furthermore, the edges of the field
have a strong effect on the psyllid distribution (Setamou and Bartels, 2015)
48
Figure 3-8. Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using Inverse distance weighting method.
Figure 3-9. Distribution of Asian citrus psyllid under grid pattern with horizontal traps position using ordinary kriging method.
49
Figure 3-10. Distribution of Asian citrus psyllid under grid pattern with horizontal traps
position using simple kriging method.
Figure 3-11. Distribution of Asian citrus psyllid under grid pattern with vertical traps
position using Inverse distance weighting method.
50
Figure 3-12. Distribution of Asian citrus psyllid under grid pattern with vertical traps
position using ordinary kriging method.
Figure 3-13. Distribution of Asian citrus psyllid under grid pattern with vertical traps
position using simple kriging method.
51
Figure 3-14. Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps
position using inverse distance weighting method.
Figure 3-15. Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps
position using ordinary kriging method.
52
Figure 3-16. Distribution of Asian citrus psyllid under zigzag pattern with horizontal traps
position using simple kriging method.
Figure 3-17. Distribution of Asian citrus psyllid under zigzag pattern with vertical traps
position using inverse distance weighting method.
53
Figure 3-18. Distribution of Asian citrus psyllid under zigzag pattern with vertical traps
position using ordinary kriging method.
Figure 3-19. Distribution of Asian citrus psyllid under zigzag pattern with vertical traps
position simple kriging method.
54
Measures of Accuracy and Effectiveness of Prediction Maps
Two indices were calculated from the measured and interpolated values at each
selected location for each test data set. Root mean squared error (RMSE) and relative
improvement (RI) considered for measuring the accuracy and the effectiveness of
prediction map. The results from table 3-4 showed that simple kriging interpolation
method is the best predictor for all different sampling methods and different traps
position because it is provided the least value of RMSE compared to other interpolation
methods. Furthermore, from the Table 3-4, it can be seen that using simple kriging
interpolation method with zigzag sampling pattern under horizontal traps position gave
us more reliable prediction since the root mean squared error is the least value (RMSE
= 5.73) compared to other interpolation methods and with other sampling pattern.
Furthermore, the best value of relative improvement was when using zigzag pattern with
horizontal traps position (RI%=0.0%). The results show that the best value of the two
indices was when using a zigzag pattern sampling with horizontal traps position.
Table 3-4. Accuracy and effectiveness measurements for the sampling patterns methods by using different interpolation methods.
Sampling patterns Traps position Interpolation method RMSE RI (%)
Grid pattern Horizontal Inverse Distance weighting 9.38 62.85
Ordinary kriging 9.47 65.37
Simple Kriging 9.33 63.85
Vertical Inverse Distance weighting 6.95 17.44
Ordinary kriging 7.32 27.91
Simple Kriging 6.72 21.45
Zigzag pattern Horizontal Inverse Distance weighting 6.33 10.50
Ordinary kriging 6.90 20.45
Simple Kriging 5.73 0.00
Vertical Inverse Distance weighting 8.57 50.0
Ordinary kriging 8.85 74.36
Simple Kriging 5.85 15.28
55
The conclusions of this study can be summarized as follow: monitoring with
sticky traps and the use of spatial maps of ACP population distribution are two important
tools to detect Asian Citrus Psyllid and predict their infestation. Sticky traps method and
GIS technology were used to estimate Asian Citrus Psyllid population in non-organic
citrus groves in Florida in summer 2015. The maps developed in this study can serve as
a database to find out the better insect pest sampling pattern which provide the less
prediction error and better validation. In addition, these maps can assist the citrus
producers in implementing pest management strategies because knowing the exact
locations of high infestation by ACP is important to restrict insecticide applications only
to places where they are necessary. The maps obtained from inverse distance
weighting using different sampling patterns with different traps positions show clearly
the map from zigzag sampling pattern with vertical traps position is better than the other
sampling pattern since its high accuracy of prediction map. Therefore, the zigzag
sampling pattern with horizontal traps position along with using simple kriging method
for interpolating Asian citrus psyllid population distribution is the most accurate one.
It can conclude from the output of the field experiment that the zigzag pattern
sampling technique along with horizontal traps position provides a best estimation for
Asian Citrus Psyllid infestation and their distribution over study area.
56
CHAPTER 4 EVALUATION OF MEASURED ACCELERATION PRODUCED BY THE
CONVENTIONALTAP METHOD AND A DEVELOPED SHAKING MACHINE FOR ASIAN CITRUS PSYLLID SAMPLING
Tap sampling method is one of the manual techniques used for monitoring Asian
Citrus Psyllid. Tapping is commonly recommended for monitoring adults of ACP. Tap
sampling is one of the effective sampling methods for detect Asian citrus psyllid (Stansly
et al., 2010). The procedure for this method is to place the clipboard beneath the branch
and strike the branch with the pipe three times and then the number of the falling adults
on the clipboard will be counted and recorded (Stansly et al., 2010)
The ultimate goal of this study is to develop a machine for quick and cost
effective mapping and monitoring psyllid population density and distribution in citrus
orchard and specific goal was to identify the vibration characteristics of tap method and
develop a machine that can simulate the same vibration characteristics. This study
determined the relationship between striking the branches and acceleration and if a
relationship exists between the length of time the branches are struck and acceleration.
Also, the optimal shaking frequency was determined by using the developed shaking
machine.
Materials and Methods
In this study two experiments were performed in the same study area. Both experiments
will be discussed in more details in this chapter.
Tap Method Experiment
Study Site. The experiments were conducted in citrus grove at the Citrus
Research and Education Center in Lake Alfred, University of Florida located at 28° 105'
84'' N 81 71' 56'' W on December 2015 - February 2016. The trees are Valencia and
57
was planted in 2012. The total area of the research plot used in this study was 0.655 ha
(1.62 ac).
Figure 4-1. Study area.
Tap Method Experiment
A modified version of the tap sampling method of Stansly et al., 2010 was
performed, with the exception that no insects were collected. Accelerometer sensors
were placed on the limb in order to sense and report the acceleration. A rod (Figure 4-2)
was used to strike the branch for fixed length of time (5, 10, and 15 s). The diameter
(𝑑𝑒) and length (𝑙𝑏) of each limb as well as the canopy diameter (𝐷𝑐) were measured
(Figure 4-3). The three trials were performed at different shaking duration for three limb
s lengths. The hitting frequency and acceleration were collected for each limb. The
procedure for the measurements was repeated three times to achieve precise results
which resulted in a 27 samples in total. As shown in Figure 4-4, the accelerometer
sensors were attached to a programmed Arduino UNO R3 board as data logger which
is attached via a USB port on a laptop to record the data using open source Cool Term
software (Version 1.4.6). The data collected using the data logger then were corrected
by removing the unwanted buffered data from beginning and the end part of the data
58
package. Then, developed code on MATLAB 2016a (R 2016a, 9.0.0.341360, 1984-
2016) software was used to produce the resultant from raw data, visualize and analyze
the processed data. The setup of the tap method experiment showed in the Table 4-1.
Figure 4-2. Rod used for shaking the limbs.
Figure 4-3. Experimental setup.
59
Figure 4-4. Circuit diagram for connecting accelerometer sensors to Arduino UNO R3
and connecting Arduino UNO R3 to a laptop via a USB cable.
The duration of the shaking and the limb length were selected as two important
parameters to define the acceleration exerted on the limb using the tap method as
shown in the table 4-1.
Table 4-1. Setup of the tap method experiment
Shaking duration (s) Limb length (m) No. of samples
5 0.889 9
10 0.990 9
15 1.143 9
The resultant acceleration (𝑚 ∙ 𝑠−2) was calculated using Equation (4-1) (Snieder,
2004).
𝑎𝑟 = √𝑎𝑥 2 + 𝑎𝑦
2 + 𝑎𝑧2 (4-1)
Where:
𝑎𝑟 is resultant acceleration (𝑚 ∙ 𝑠−2),
𝑎𝑥is acceleration along the x-axis (𝑚 ∙ 𝑠−2),
𝑎𝑦 is acceleration along the y-axis (𝑚 ∙ 𝑠−2),
60
𝑎𝑧 is acceleration along the z-axis (𝑚 ∙ 𝑠−2).
Accelerometer
A microelectromechanical accelerometer (Freescale Semiconductor, 2008) with
low voltage applied to it was used to sense and record the acceleration in three
perpendicular directions for each limb (Table 4-2). The features of the accelerometer
are: low voltage operation of 2.2-3.6 V, maximum acceleration for all axes of ± 2000 g,
supply voltage range of -0.3 to +3.6 V, and the low cost. The accelerometer
measurements can be acquired in units of m/s^2 or in g-forces (g).
Table 4-2. Accelerometer parameters
Parameters Target value
Number of axes 3 Maximum acceleration amplitude ± 6 g Acceleration resolution 0.01 g
Determination of Tree Limb Parameters
The parameters of the selected tree limbs were measured and described in the
Table 4-3.
Table 4-3. Tree limb parameters
Limb length, 𝐿𝑏 (m)
1st (0.889) 2nd (0.990) 3rd (1.143)
Start point diameter (𝑑𝑐) 0.020 0.031 0.027
Endpoint diameter (𝑑𝑒 ) 0.008 0.014 0.009
Canopy diameter (𝐷𝑐) 0.317 0.317 0.317
Hitting distance (𝐿𝐻) 0.635 0.660 0.812
Data Extraction
The raw data from the accelerometer sensors were converted to decimal values
using the program code developed on MATLAB 2016a (R 2016a, 9.0.0.341360, 1984-
2016). Then, a code on MATLAB 2016a software was used to produce the acceleration
61
resultant from raw data, visualize and analyze the processed data. The graph in Figure
4-5 shows the three components of the acceleration in Cartesian coordinates. The three
Figures (a, b, c) show the acceleration along the x-axis (g), y-axis (g), and z-axis (g).
While the Figure (d) represent the resultant of acceleration(𝑎𝑟). From the Figure 4-5,
the maximum acceleration along the x-axis, y-axis is (4.8 g) and (5.8 g) for the z-axis.
From the Figure, the maximum resultant acceleration is approximately (8.3 g). Figure 4-
6 shows the statistical analysis peaks of resultant acceleration which including the
histogram, boxplot, and the normal distribution. Figure. 4-6 display the frequency of
resultant of acceleration peaks (𝑎𝑟 ). From the presented boxplot of the acceleration
resultant peaks, we can determine the median and mean of the resultant of acceleration
peaks. Moreover, the frequency of repeating same values of resultant’s acceleration
peaks can be obtained from the histogram. This is found that although the frequency is
higher at peak of accelerations about (1.4g) and (8g), the peak of normal distribution
curve is occurring about (4.5 g) which is summation of majority of peaks are happening
around this domain.
62
Figure 4-5. Acceleration wavelength: (a) 𝑎𝑥 [g]; (b) 𝑎𝑦[g]; (c) 𝑎𝑧[g]; (d) 𝑎𝑟 [g].for the tap
method.
63
Figure 4-6. Statistical analysis peaks for resultant acceleration including: Histogram, Boxplot, and normal distribution.
Shaking Machine Experiment
Mechanizing insect pest scouting is a new step can be taken into considerations.
Thus, a shaking machine was built to achieve the goal. The details of the machine will
be explained below.
Development of Limb Shaker
Design and Construction
The aim of this research was to develop a shaking machine for pest sampling
and monitoring. Work involved building a prototype of a mechanical limb shaking
machine and then evaluating it in citrus groves. The machine was designed and built in
the workshop of the University of Florida’s Citrus Research and Education Center
(CREC) in Lake Alfred, University of Florida.
Shaking machine design should follow these criteria:
64
1. The shaker will be mounted on the tractor and the shaking height adjusted through the hydraulic system of the tractor by the tractor operator.
2. The operation of the shaking system should be hydraulically operated through the hydraulic system of the tractor.
3. The shaking arm speed should be controlled by the operator manually through the control value of the hydraulic system.
4. The shaking machine will be operated on one tree and it has only one shaking mechanism.
5. The shaking machine will be operated using the hydraulic power of the tractor through hydraulic hoses connections.
6. The shaking machine operate between rows and near the selected tree to collect the data.
7. The new concept for this machine is the shaking action will be applied only on the selected limb.
The shaking machine comprised four main parts: Main frame, a hydraulic
system, power transmission system, and the shaking system. The dimensions of the
shaker frame were 1.19 m width and 0.55 m height. To mount the shaking unite, an
attachment at the front of the tractor was made to hold the shaking machine temporarily.
The main frame was attached to the main frame of the tractor using two latches, one
latch is on the left side of the frame and the other one is on the right side of the frame as
shown in Figure 4-7.
65
Figure 4-7. The front frame of the tractor attached to the main frame of the shaking machine.
The hydraulic system is composed of two main parts: a hydraulic motor and the
flow control valve. The hydraulic motor was powered by the hydraulic system of the
tractor and was mounted beneath the transmission mechanism housing as shown in
Figure 4-8. The output shaft of the hydraulic motor is attached to the driving sprocket of
the transmission system of the shaking machine. A manual hydraulic flow control valve
allowed control of the rotational speed of the driving sprocket. The adjustable flow
control valve was used to adjust the shaking frequency of the shaking arm (0.26 Hz,
2.33Hz and 4.00Hz) as shown in Figure 4-8.
66
Figure 4-8. The components of the hydraulic system of the shaking machine: A) shows the hydraulic motor; B) shows the flow control valve.
The transmission system consists of two sprockets and a single crank unit
enclosed in a housing. To operate the shaking arm, a sprocket and chain mechanism
with a ratio of 1:2 was used between the hydraulic motor and the crank shaft to operate
67
the shaking arm under different frequencies The Driving sprocket was powered by the
hydraulic motor and the rotational motion of the driven sprocket was converted into
linear motion using single -crank unit as shown in Figure .4-9. The driving sprocket with
30 teeth, while the driven sprocket with 15 teeth. The distance between the driving
sprocket and the driven sprocket is (0.20 m) and the length of the crank unit is (0.38m)
as shown in Figure 4-10.
Figure 4-9. Top view of the transmission system of the shaking machine.
68
Figure 4-10. Top view schematic of the transmission system and the shaking system
The shaking system consisted a box of (0.38 m) long, (0.61 m) width and (0.61
m) height. Inside the box, there is embedded part which is attached directly to the crank
shaft. The embedded part is attached to the box through two pivoted points from the top
and bottom of the box. One piece of shaking arm made of plastic with (1.23 m) long as
shown in Figure 4-11. and can be attached directly to the box frame through one-point
hitch linkage as shown in Figure 4-12. The shaking arm is pivoted at one end to allow
the shaking arm moving in semi arc around the pivot point.
Figure 4-11. Rod used for shaking the limbs.
69
Figure 4-12. Components of shaking system.
Since the primary goal of building the shaking machine is to monitor the insect
pest. Then, a clipboard was built to collect the psyllids. The clipboard plate with
dimensions (0.25×0.38 m) made from aluminum attached to the stick with length (0.56
m) as shown in Figure. 4-13. The stick attached to the bottom of the box as shown in
Figure 4-14. The clipboard placed beneath the shaking arm with a distance of (0.33 m)
70
from the shaking arm. A sticky trap will be placed above the clipboard during the field
test as shown in Figure 4-15.
Figure 4-13. Clipboard tool and its dimensions.
71
Figure 4-14. Side view of the shaking machine showing the shaking arm.
Figure 4-15. Components of the shaking machine.
72
Field Experiment
To find the optimal system operation, an experiment was conducted using three
shaking frequency (0.26, 2.33 and 4.00 HZ) and three shaking duration (5, 10, and 15s).
The duration of the shaking and the shaking frequency were selected as two important
parameters to define the acceleration exerted on the limb using the developed shaking
machine. First, the frequency of the shaking arm was selected through the flow control
valve in order to determine the speed of the shaking arm. The speed of the driving
sprocket was measured using Digital Tachometer (Model CDT-2000HD). The error
measurement using digital tachometer ranged from (0.04 – 0.08). The shaker was
operated between the rows outside the trees. Accelerometer sensors were placed on
the limb in order to sense and report the acceleration exerted on the limb during shaking
procedure. A shaking arm was used to strike the branch for fixed length of time (5, 10,
and 15 s) under different shaking frequencies. The procedure for the measurements
was repeated three times to achieve precise results which resulted in a 27 samples in
total. As shown in Figure 4-16, the accelerometer sensors were attached to a
programmed Arduino UNO R3 board as data logger which is attached via a USB port on
a laptop to record the data using open source Cool Term software (Version 1.4.6). The
data collected using the data logger then were corrected by removing the unwanted
buffered data from beginning and the end part of the data package. Then, a code on
MATLAB 2016a software was used to produce the resultant acceleration from raw data.
73
Figure 4-16. Experiment Setup
Table .4-4. Parameters of the limb and sensor location for the shaking machine experiment
Start point diameter (𝑑𝑐) 0.025 m
Endpoint diameter (𝑑𝑒 ) 0.007 m
Canopy diameter (𝐷𝑐) 0.736 m
Limb length, 𝐿𝑏 0.939 m
The distance of the sensor( 𝐿𝑠) 0.431 m
Table 4-5. Setup of the shaking machine method experiment
Shaking duration (s) Shaking Frequency(Hz) No. of samples
5 0.26 9
10 2.33 9
15 4.00 9
Data Extraction
The raw data from the accelerometer sensors were converted to decimal values
using the program code developed. Then, a code on MATLAB 2016a software was
used to produce the resultant acceleration from raw data.
Figure 4-17 shows the statistical analysis peaks which including the histogram,
boxplot, and the normal distribution using the shaking machine. Figure 4-17 display the
74
frequency of resultant acceleration peaks (𝑎𝑟 ). From the presented boxplot of the
resultant acceleration peaks, we can determine the median and mean of the resultant
acceleration peaks. Moreover, the frequency of repeating same values of resultant
acceleration peaks can be obtained from the histogram. This is found that although the
frequency is higher at peak of accelerations about 0.7, 1.3 and 1.7g., the peak of normal
distribution curve is occurring about 1.9 g which is summation of majority of peaks are
happening around this domain. The graph in Figure 4-18 shows the three components
of the acceleration in Cartesian coordinates. The three Figures (a, b, c) show the
acceleration along the x-axis (g), y-axis (g), and z-axis (g). While the Figure (d)
represent the resultant acceleration in r. From the Figure 4-18, the maximum
acceleration along the x-axis, y-axis is (3 g) and (5.1 g) for z-axis. From the Figure, the
maximum acceleration resultant is approximately 5.2 (g).
00
10
15
20
25
1 2 3 4 5 6 7 8 9 10
5
Peaks of ar [g]
Fre
quen
cy
Average
Median
Histogram
Normal Distribution
Figure 4-17. Statistical analysis peaks including: Histogram, Boxplot, and normal distribution.
75
6
8
10
12
4
2
0
Time [s]5 10 150
(d)
ar
[g
]
0
2
4
6
-2
-4
-6
Time [s]5 10 150
(b)
ay
[g
]
0
2
4
6
-2
-4
-6
Time [s]5 10 150
(a)a
x [
g]
0
2
4
6
-2
-4
-6
Time [s]5 10 150
(c)
az
[g
]
Figure 4-18. Acceleration wavelength: (a) 𝑎𝑥 [g]; (b) 𝑎𝑦[g]; (c) 𝑎𝑧[g]; (d) 𝑎𝑟 [g].for the
shaking machine method.
Results and Discussion
Tap Method Experiment Results
In terms of data analysis, to analyze the results of the experiment using IBM
SPSS statistics version 23. A univariate generalized linear model (GLM) was used to
determine significant difference at 0.05 significance level. The results of the analysis of
variance (ANOVA) for the effects of shaking duration and the limb lengths on the
acceleration are shown in Table 4-6.
76
Table 4-6. The output of the ANOVA analysis for shaking time and limb length using the tap method.
Source Sum of Squares
df Mean Square
F Significance
Limb 3.070 2 1.535 1.782 0.197
Time 0.673 2 0.337 0.391 0.682
** Significant at P< 0.05
The products of three limb lengths (0.889, 0.990, 0.143 m) and three shaking
durations (5, 10, 15 s) were analyzed using IBM SPSS statistics version 23 to study the
effect of shaking time and limb length and their interactions as function of the amount of
acceleration under the experimental conditions. As shown in Table 4-6, according to the
ANOVA, there were non-significant main effect of limb length F (2, 18) = 1.782, p = 0.05
on the amount of the acceleration. For the shaking duration, there was no statistically
significant effect on the acceleration, F (2, 18) = 0.391, p = 0.05. Figure 4-19 displays
the effect of limb length and shaking duration on acceleration. The red line represents
the limb with length 1.143 m, green line represents the limb with length 0.990m and the
blue line represents the limb with length 0.889 m under shaking for different durations
(5,10 and 15 sec).
Shaking Machine Experiment Results
A univariate GLM was used to determine if a significant difference exists at 0.05
level of significance between the shaking frequency and the shaking duration and
acceleration. The results from Table 4-7 shows that there are significant differences for
the shaking duration F (2, 18) =6.258, p=0.05, and the shaking frequency has a
significant difference on the acceleration (2, 18) =418.532, p=0.05 since the force
applied on the limb through shaking procedure will increase with increasing the shaking
frequency of the shaking arm (increasing the speed of the shaking arm).
77
Figure 4-19. The plot of acceleration for each combination of groups of shaking time and limb length. Limb length is represented as: red line, 0.889 m; green line, 0.990 m; and blue line, 1.143 m.
Figure 4-20 is a multiline graph which shows the relationship between the
shaking frequency, shaking duration and acceleration. Figure 4-20 shows that shaking
the limb at a frequency of 4.00 Hz results in a higher acceleration than shaking the limb
at 0.26 Hz and 2.33 Hz. Also, there is a significant difference in acceleration while
shaking the limb with different shaking duration. From Figure 4-20 it can be seen that
the first shaking frequency level (0.26 Hz) has very little effect on the acceleration. The
average acceleration exerted on the limb using shaking frequency of (0.26 Hz) with (5
78
sec) duration is approximately (0.066), with shaking for (10 sec) the acceleration
average is (0.071 g) and for (15 sec) shaking the acceleration average is about (0.072
g). While shaking the limb at (2.33 Hz) was the better as they resulted in significantly
higher acceleration than at (0.26 Hz). The average of acceleration exerted on the limb
using (0.26 Hz) with (5 sec) duration is (0.896 Hz), with (10 sec) shaking, the average of
acceleration is (0.759 g) and for shaking (15 sec) the acceleration average is (0.711 g).
The highest level of acceleration resulted from shaking the limb with a frequency of
(4.00 Hz). The acceleration average of shaking for (5 sec) is about (2.523 g), for
shaking the limb (10 sec) the average of acceleration is about (2.014) and shaking the
limb for (15 sec), the acceleration average is (1.98 g).
Figure 4-20. The plot acceleration for each combination of groups of shaking time and
shaking frequency. The blue line represents shaking the limb for 5 s, while the red line represents shaking the limb for 10 s, and the green line represents shaking the limb for 15 s.
79
Table 4-7. The output of the ANOVA analysis for shaking duration and shaking frequency using the shaking machine.
Source Sum of Squares df Mean square F Significance
Time 0.308 2 0.154 6.258 0.009 Frequency 20.614 2 10.307 418.532 0.000
** Significant at P< 0.05
The conclusion of the study using the tap method can be summarized as follow:
Two studies were conducted to find the relationships between shaking duration, the
length of the tree limb, and the shaking frequency on acceleration. A total of 54 samples
were selected randomly in a citrus grove in Lake Alfred to accomplish the objectives of
both studies.
1. With regards to the limb characteristics, it was found that the limb length does not have a significant effect on the acceleration performed on the limb at 0.05 level of significance.
2. Further studies are needed to investigate the effect of other important parameters such as the diameter of the limbs on the required acceleration.
The conclusions of shaking machine study can be summarized as follows:
1. The shaking machine can be lowered and raised by the hydraulic system of the tractor.
2. The speed and the height of the shaking arm can be adjusted by the operator of the tractor.
3. The ease of positioning the shaking arm aided in minimizing the time required to shake the selected trees.
4. The effect of the shaking duration and shaking frequency on the acceleration was significant at 0.05 level of significance.
5. As the shaking frequency increased the acceleration increased; however, the frequency of 0.26 Hz resulted with low acceleration.
6. For next experiment, it is recommended to use frequency of (2.33 Hz) which can give a better acceleration required for falling psyllids onto the clipboard.
80
CHAPTER 5 UTILIZING A DEVELOPED SHAKING MACHINE AND DIFFERENT INTERPOLATION
TECHNIQUES FOR MONITORING AND MAPPING ASIAN CITRUS PSYLLID
The density of pest populations is varied spatially at any time within a field since
the factors affecting such as landscape, soil, crop, and environmental factors are not
constant (Park et al.,2007). Interpolation plays a role in predicting the unknown values
from sample points (ESRI, 2016). Inverse distance weighting (IDW), Kriging (K), Natural
neighbor, Spline, Topo to raster and trend are the common and available interpolation
methods have been used (ESRI, 2016).
Inverse distance weighting is a deterministic, nonlinear interpolation method. This
method based on estimating the unknown value at unsampled locations from the
weighted average of the sample points of neighborhood (Gruver and Dutton, 2014).
There are some factors need to be taken in to our considerations when using inverse
distance weighting technique including: the relationship of the phenomenon and the
distance, and the size of the neighborhood (Gruver and Dutton, 2014). The output of the
prediction method using this technique can be influenced by the spacing and the density
of the sample points (Gruver and Dutton, 2014). IDW interpolation estimates the
unknown values with a mathematical formula from the nearby knowing values as shown
in Equation 5- 1and 5-2 (Yasrebi et al., 2009):
Z(x)=∑ 𝑊𝑖 𝑍𝑖
∑ 𝑊𝑖
(5-1)
𝑊𝑖 = 1
𝑑𝑖𝑝 (5-2)
Where:
81
Z: is the unknown value.
𝑊𝑖: is the measured value.
𝑑 : is the distance between the sample point and the unknown point.
P: power parameter.
Ordinary kriging is one of the kriging interpolation methods. This method uses
smivariogram or covariance to define the autocorrelation. In this method, the mean is
constant in the local neighborhood with minimum error variance (Lefohn et al., 2005).
Equation 5-3 (Yasrebi et al., 2009) used to estimate the predicted value using ordinary
kriging method.
Z(x)= ∑ 𝜆𝑖 𝑍𝑖𝑁𝑖=1 (5-3)
Where:
Z(x): The predicted value
Zi: The measured value at location i
𝛌: unknown weight for the measured value at the i location
N: The number of measured values.
Determine the phenomenon measurements can be costly, laborious and time
consuming. It is important to predict the values where the observations are not
available. Therefore, using interpolating technique is the solution to obtain the value of
the phenomenon at a location where data are not available using the sample point’s
data (Krivoruchko, 2012). There are two categories for the interpolation method
including: The deterministic methods and the probabilistic methods. Scouting for adult
Asian Citrus Psyllid are usually depends on visual counts or using sticky traps or sweep
82
net or using the tapping method. In this study a modified version of tap sampling
method was used for scouting Asian Citrus Psyllid adults and investigate the existing
infestation. The ultimate goal for this study is to utilizing the shaking machine for
monitoring Asian Citrus Psyllid in citrus groves and evaluate the accuracies of the
generated maps under different interpolation methods.
Material and Methods
Study Area
An experiment test was performed in citrus groves located at Citrus Research
and education center (CREC) in Lake Alfred, Florida located at latitude 28˚ 7' 50.78" N,
longitude 81˚ 43' 1.95" W (North-40 field) on May 2016. Weather conditions on study
area was with 25.62⁰C averaged temperature and 5.5 mph averaged wind speed. The
total area of the research plot used in this study was 0.655 ha (1.62 ac) s shown in
Figure 5-1. The citrus trees are Valencia and planted on 2012.
Figure 5-1. Study area.
83
Insect Sampling Procedure and Data Collection
Zigzag Pattern Sampling:
A new sampling technique were implemented in the field to monitor ACP on May
2016. One of the common methods to monitor ACP in citrus trees is using yellow sticky
traps. A yellow sticky traps were used on specific location in the field to follow the
zigzag pattern to monitor ACP as shown in the Figure 5-2. The yellow sticky traps (0.20
by 0.26 m) were placed on the clipboard tool of the developed shaking machine to
capture the fallen psyllids adults during the scouting procedure. Two different data set
were employed in the field. The first data set was consisted from 42 locations was
distributed in the field that gives the zigzag pattern. The second data set consisted
from15 randomly selected trees for measuring ACP which represent as a reference
points to create the true population of ACP over the study area. In the Figure 5-2, each
symbol in the point layer represents a location where the ACP has been measured. The
ACP was collected by implement the mechanized tap method. The number of adult
psyllid captured on each sticky traps are counted manually.
Figure 5-2. Distribution of sample points representing the location of Asian Citrus Psyllid monitoring points.
987
654321
4241403938373635
3433323130
292827262524
232221201918
171615141312
1110
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Sampling Technique
A new sampling technique was used which simulate the tap sampling method. In
this new technique, the developed shaking machine was used to simulate the tapping
method. The tractor-mounted limb shaker allowed the placement of the limb shaker to
be adjusted to the height of the limb using the hydraulic system of the tractor. The
shaker was operated between the rows outside the trees. The frequency of the shaker
was selected through the flow control valve in order to determine the speed of the
shaking arm. A frequency of (2.33 Hz) was selected based upon the results of a prior
field test conducted using this machine. Then, the operator positioned the shaker near
the endpoint of the selected limb. One of yellow sticky traps were placed on the
clipboard to catch the fallen psyllids as shown in Figure 5-3. Then, the shaking arm was
operated to shake the limb for a set period of time in the horizontal plane. A number of
psyllids fallen on to the sticky traps during shaking the limb. After each trial, the sticky
trap was warped with plastic wrap for laboratory counting. The number of adult psyllid
captured on each sticky traps are counted manually and then analyzed.
Figure 5-3. Developed shaking machine used for collecting Asian citrus psyllid in the field.
85
Recognizing Psyllids on Sticky Traps
One of the tasks of this experiment is to identify and count the psyllids in each
sticky trap. When traps were retrieved, they were covered with plastic wrap and
collected for counting of ACP in the laboratory. When evaluating the yellow sticky traps,
a magnifying glass was used to help recognize the psyllids from other insects and
debris on the card. Figure 5-4 shows the yellow sticky traps with (0.20 by 0.26 m) used
for this purposes.
Figure 5-4. Yellow sticky trap used for capturing Asian citrus psyllid.
86
Geostatistical Method for Interpolating Asian Citrus Psyllid Distribution.
The interpolation process was applied to the datasets using four different
interpolation techniques including: Inverse distance weighting, ordinary kriging, and
simple kriging to create the prediction map for psyllids. These methods were employed
to compare their performances for interpolating ACP distribution. Geostatistical
analyses were employed using the geostatistical analyst extension in ArcMap
(Arcgis10.3.1 © 1999-2015 Esri Inc. The measure of the accuracy, called the Root-
mean -squared error (RMSE) is one important measurements used for to test the
prediction map. The best interpolation method will be determined based on the smallest
root mean squared error obtained from each interpolation technique. Equation 5-1
shows the mathematical formula used for RMSE estimation (Odeh et al., 1994).
RMSE =√1
𝑛 ∑ (|𝑧(𝑥𝑖 ) − ��𝑛
𝑖=1 (𝑥𝑖 )|)2 (5-1)
Where
RMSE: Root-mean –squared-error
Z (Xi): the observed value at location i
��(Xi): the predicted value at location i
N: the sample size
Cross validation can be used to evaluate the results of interpolation techniques.
The cross validation is based on calculating the percent error (PE) (%) (Lu and Wong,
2008). The percent error can be calculated using equation (5-2) (Lu and Wong, 2008).
PE (%) = 𝑅𝑀𝑆𝐸
(1
𝑁) ∑ (𝑁
1 ��𝑖) × 100 (5-2)
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Where:
PE: Percent error (%)
RMSE: Root-mean –squared-error
Pi: Observation value
N: Number of observations
Insect sampling, sample locations georeferenced, then by looking at the insect
count results, it is possible to correlate the insect population with spatial data. By using
Real time kinetic (RTK) GPS system, the location of each Ground Control Points (GPS)
is defined on a topographic map.
Result and Discussion
Exploratory Statistics for Asian Citrus Psyllid under Different Sampling Patterns with Different Traps Position
The summary statistics of ACP distribution using zigzag sampling patren with
horizontal traps position are shown in Table 5-1. The positive value of skewness and
kurtosis value is (0.838) and (2.924) respectively.
Table 5-1. Descriptive statistics for ACP distribution using zigzag sampling pattern. Sampling method Traps position Min Max Mean Std.dev Skewness Kurtosis Median
zigzag pattern Horizontal 0 9 2.738 2.479 0.838 2.924 2.5
Prediction of Asian Citrus Psyllid Distribution.
The number of adult psyllid captured on each sticky traps are counted manually
and then analyzed. The interpolation process was applied to the datasets using three
different interpolation methods including: Inverse Distance Weighting (IDW), Ordinary
kriging (OK) and simple kriging (SK) to estimate unknown ACP in the neighborhood.
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The results of the interpolation were represented over the study site map which shown
later.
Surface Mapping
By using the zigzag pattern of traps distribution in the field to capture Asian
Citrus Psyllid. The spatial distribution of Asian Citrus Psyllid produced by different
interpolation methods from samples taken at 42 locations are shown in the Figures (5-5,
5-6 and 5-7) which is due to many factors. Each prediction map provide insect
population distribution represented by a specific color on the map which is shown in the
accompanying key. The minimum value (zero) represents the lowest number of psyllids
and the maximum value (9) represents the maximum value of psyllids in three
interpolation techniques. The possible explanations for the high infestation with adult
psyllids in certain areas of the field is the new flush and the temperature which effects
on the psyllids population (Rogers and Stansly ,2006) and psyllid population correlated
positively with the relative humidity and the emergence of new leaves (Martini et
al.,2016). Furthermore, the edges of the field have a strong effect on the psyllid
distribution (Setamou and Bartels, 2015)
Measures of Accuracy of Prediction Maps
The prediction error mean and the root mean squared error can be seen in the
Figure 5-8 and Figure 5-9 for different interpolation techniques and sumrazied in Table
5-2 .Figure 5-8 shows that the mean is ( = 0.178) for Inverse weighting distance and
RMSE= 2.736, and for the ordinary kriging interpolation , mean =0.246 and RMSE =
2.925. For the simple kriging, the mean = 0.046 and RMSE =2.651.
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Figure 5-5. Prediction map of Asian citrus psyllids using inverse distance weighting
technique.
Figure 5-6. Prediction map of Asian citrus psyllids using ordinary kriging technique.
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Figure 5-7. Prediction map of Asian citrus psyllids using simple kriging technique.
The performance of the methods was assessed using two indices which
calculated from the measured and interpolated values at each selected location for data
set. The accuracy was measured by the root mean squared error (RMSE) and Percent
Error (PE) for 15 validation data set selected as shown in Table 5-3. As interpreted from
Table 5-3, RMSE and PE (%) for different interpolation techniques are not the same.
From Table 5-3, there is no differences between ordinary kriging and simple kriging
since the results was the same in terms of RMSE and PE. it can be concluded from the
Table 5-3 that Inverse weighting distance (IDW) is more accurate for predicting the
spatial distribution of Asian citrus psyllid than other methods since RMSE= 2.708 and
PE = 0.864 which they are the lowest values compared to other methods. Figure 5-10
support that IDW method performs better than OK and SK methods for prediction since
percent error was the lowest value using IDW (=86.42).
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Table 5-2. The means of mean (M) and root mean square error (RMSE) for different interpolation methods.
Interpolation methods
Prediction error
M RMSE
Inverse distance weighting 0.178 2.736
Ordinary kriging 0.246 2.925
Simple kriging 0.046 2.651
Table 5-3. RMSE, Error (%) and RMSE for different interpolation methods.
Interpolation technique PE (%) RMSE
Inverse Distance Weighting 86.42 2.708 Ordinary Kriging 96.45 3.022 Simple Kriging 96.09 3.011
Figure 5-8. The cross validation comparison of the ACP distribution map between inverse distance weighting and simple kriging method.
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Figure 5-9. The cross validation comparison of the ACP distribution map between inverse weighting distance and ordinary kriging.
Figure 5-10. The percent error with different interpolation methods for ACP prediction.
The conclusions of this study can be summarized as follow:
This study aimed to implement shaking machine for monitoring Asian Citrus
Psyllid and compare different interpolation methods for generating surface maps of
86.42
96.45 96.09
80
82
84
86
88
90
92
94
96
98
IDW OK SK
Err
or
(%0
Interpolation methods
93
Asian Citrus Psyllid. The data set consisted of 42 selected tree location in a zigzag
pattern as long as 15 tree locations was selected as reference points where the number
of ACP were measured. IDW, OK and SK interpolation techniques were used in
producing the surface maps for ACP density over the study area. The accuracy of the
prediction maps was measured using three measurement indices. The results obtained
from inverse distance weighting, ordinary kriging interpolation and simple kriging show
clearly the inverse distance weighting is better than the other interpolation methods
since its provide more accurate prediction than other interpolation methods.
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CHAPTER 6 ASIAN CITRUS PSYLLID MONITORING CALCULATIONS
The advantage of the developed shaking machine as an alternative method for
ACP monitoring is to reduce time and labor cost. In this section, the focus will be on
operation cost and its effects on the total labor cost for the farmer.
The shaking machine is designed to reduce the sampling cost in the field. In
terms of labor, implementing the shaking machine will allow the field to be monitored
with fewer workers. The largest benefit of using the shaking machine for ACP sampling
is to improve work efficiency by reducing technician work time in the field.
Field Capacity and Efficiency
The field capacity of farm machinery is defined as the rate of the work per hour
and is mostly often measured in acres per hour of operation (Hanna, 2016). Theoretical
field capacity (TFC) is based on using the full width and the travel speed of the
machine. The unit of TFC are in acres per hour and can be expressed as:
TFC = Width (ft)×speed (
mi
hr)
8.25
(6-1)
The effective field capacity is the ability of the machine to do the function under
field conditions and it is always less than the theoretical field efficiency due to many
factors such as machine adjustment, lubrication and refueling during the day, repairs,
and turning…etc. Field efficiency (FE) is the ratio of the actual field capacity (EFC) to
the theoretical field capacity (TFC) (Hanna, 2016). Field efficiency is not constant due to
many factors including: size and shape of the field, crop yield, crop condition, moisture
and the pattern of field operation
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FE (%)=EFC
TFC × 100
(6-2)
Comparison of the Labor Cost Using the Conventional Tap Sampling Method Versus the Shaking Machine Method
An algorithm was developed in MATLAB to compute the time required for
sampling the ACP for both the conventional tap sampling method and the shaking
machine method.
It can be seen from Figure 6-1 that utilizing the shaking machine system required
less time than the conventional tap sampling method to accomplish a task. Hours of
labor using tap method exceeded that of the shaking machine by 160 percent because
travel speed is lower and the time required for sampling is higher for the tap method
than that of the shaking machine. The shaking machine runs at an average speed of 10
mph and 0.91 min, i.e. the total time for monitoring ACP, while the tap method has an
average travel speed of 1.78 mph and 1.88 min to monitor ACP. From the Figure 6-1,
the shaking machine required 45 minutes (less than an hour) for ACP sampling, while
for tap method required approximately 117 min for sampling ACP for the same size area
and with the zigzag pattern. Consequently, labor cost can be calculated by multiplying
the labor wage rate times the number of hours. Since the labor cost is relatively fixed, t
the total cost can be determined. Then, by multiplying the labor cost by the hours
required to finish the work, the cost can be determined for both methods. Currently, a
Florida grower pays ($15.00) per hour for an operator to perform the tap method in the
field.
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It can be concluded that the labor cost for the shaking machine for the study area
using the zigzag pattern to monitor 42 trees is approximately $15, whereas using the
tap method for the same area is $30.
The calculations listed above did not include machine preparation, travel time to
and from the field, turning, and other delays. Also, for the tap method, the operator
brakes time is not included in the calculations.
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Figure 6-1. Comparison of two different methods on the total time required to monitor Asian citrus psyllid in citrus groves.
The Blue line represents the tap sampling method and red line represents shaking machine method.
98
CHAPTER 7 CONCLUSIONS AND FUTURE WORK
Conclusions
The main objectives of this study were to build a shaking machine for quick and
cost- effective monitoring and mapping the ACP distribution in citrus groves. Work
involved building a prototype of a mechanical limb shaking machine and then utilizing it
in citrus groves. The machine was developed and fabricated in the workshop of the
University of Florida, Citrus Research and Education Center (CREC) in Lake Alfred,
Florida.
Besides building the shaking machine, four main experiments were conducted in
the field. The first experiment was conducted in order to develop sampling techniques in
terms of optimal placement (horizontal, and vertical) for the colored sticky traps that
monitor ACP using different sampling patterns (grid and zigzag), generating geo-insect
prediction maps in citrus groves in Florida.
The second and third experiments were conducted to determine the relationship
between striking the branches and acceleration. A total of 54 samples were randomly
selected in a citrus grove in Lake Alfred to accomplish the objectives of both studies. In
the tap sampling method experiment, a modified version of the tap sampling method of
Stansly et al., (2010) was performed with the exception that no insects were collected.
Accelerometer sensors were placed on the limb in order to sense and report the
acceleration. A rod was used to strike the branch for a fixed length of time (5, 10, and
15 s) for different limb lengths. The results showed that the effect of limb length and
shaking time on the amount of the acceleration was not significant at the 0.05
significance level.
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To find the optimal system operation, an experiment was conducted with three
shaking frequencies of (0.26, 2.33 and 4.0 Hz) and three shaking durations (5, 10, and
15s). Accelerometer sensors were placed on the limb to sense and report the
acceleration. The accelerometer sensors were attached to a programmed Arduino UNO
R3 board as data logger which is attached via a USB port on a laptop to record the data
using open source Cool Term software (Version 1.4.6). The data collected using the
data logger then were corrected by removing the unwanted buffered data from the
beginning and the end part of the data package. Then, a code in MATLAB 2016a
software was used to produce the resultant from raw data, and visualize and analyze
the processed data. The results show that there are significant differences for the
shaking duration and shaking frequency at the 0.05 level of significance.
The fourth experiment objectives were to utilize the shaking machine to monitor
ACP in citrus groves and evaluate the accuracies of the generated maps under different
interpolation methods. IDW, OK and SK interpolation techniques were used in
producing the prediction maps for ACP over the study area. Two indices were used to
assess the performance of the interpolation methods: root -mean -squared error
(RMSE) and percent error (PE). The map from IDW method is the accurate than the
other two interpolation methods since their high accuracy of prediction map.
Future Work
improvement of shaking machine can be reached by reducing the time required
for ACP sampling in the citrus groves. This step can be achieved through developing an
image processing system to count ACP attached to the sticky traps. This can be done
using a high resolution digital camera that enable taking a high-resolution image of
sticky traps during ACP sampling. These digital images will provide a quick identification
100
of insect infestation. This will also improve economic efficiency in the future which help
to reduce labor time, enable a large amount of the samples to be processed, and
provide consistent output and cost savings due to the improvement in the work
efficiency of the operator especially for the large area.
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APPENDIX MATLAB CODES
The following function was used to determine the total time required for
monitoring Asian citrus psyllid using tap method and shaking machine method.
clear close all clc
%% distance =
[0;74.06;76.27;77.39;37.31;39.29;38.49;74.21;74.21;76.59;36.90;10.32;36.11;38
.89;75;36.11;78.18;31.74;37.31;44.44;69.45;40.48;74.22;16.31;36.11;76.59;33.3
4;37.31;80.18;26.20;74.22;37.70;37.31;79.77;23.44;37.30;40.88;40.08;33.75;36.
91;40.48;36.57]; % m machin_speed = 268.22; % m/min time_move_machin = distance/machin_speed; tap_speed = 48; % m/min time_move_tap = distance/tap_speed;
tap = 1.88; machin = 0.91; tap_distance_time = 0; machin_distance_time =0;
for n = 1:42 machin_time(n,1) = n*machin; tap_time(n,1) = n*tap; if (n>1) machin_distance_time(n,1) =
machin+time_move_machin(n,1)+machin_distance_time(n-1,1); tap_distance_time(n,1) = tap+time_move_tap(n,1)+tap_distance_time(n-
1,1); end end
figure(1) hold on box on plot(tap_distance_time) plot(machin_distance_time) ylabel('Time(minutes)') xlabel('Tree number') legend('Tap method','Machine Method')
The following function was used to determine the resultant acceleration for tap
method and shaking machine method.
102
%% Load data Filename = 'data.txt'; Delimiter = ','; startRow = 2; formatSpec = '%f%f%f%f%f%f%f%f%f%f%f%f%f%[^\n\r]'; fileID = fopen(filename,'r'); dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'HeaderLines' ,startRow-1, 'ReturnOnError', false); fclose(fileID); t_ms = dataArray{:, 1}/1000; sensor1.x = dataArray{:,2}-mean(dataArray{:,2}); sensor1.y = dataArray{:,3}-mean(dataArray{:,3}); clearvars filename startRow delimiter formatSpec fileID dataArray ans; %% Plot data figure('Name','Cartesian (x,y,z) Coordinates') % x data subplot(3,1,1) hold on plot(t_ms,sensor1.x,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-6,6]) ylabel('a_{x}, [g]') title('(a)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') % y data subplot(3,1,2) hold on plot(t_ms,sensor1.y,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-6,6]) ylabel('a_{y}, [g]') title('(b)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') % z data subplot(3,1,3) hold on plot(t_ms,sensor1.z,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-6,6])
103
ylabel('a_{z}, [g]') title('(c)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') %% Plot data figure('Name','Spherical (r,theta,phi) Coordinates') % r data for i=1:size(t_ms,1) sensor1.r(i,1)=sqrt((sensor1.x(i,1)^2)+(sensor1.y(i,1)^2)+(sensor1.z(i,1)^2)); end subplot(3,1,1) hold on plot(t_ms,sensor1.r,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([0,10]) ylabel('a_{r}, [g]') title('(a)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') % theta data for i=1:size(t_ms,1) sensor1.theta(i,1)=atan((sensor1.y(i,1)^2)/(sensor1.x(i,1)^2))*(182/pi); end subplot(3,1,2) hold on plot(t_ms,sensor1.theta,'color',[1,0.3,0]) xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-180,180]) ylabel('a_{\theta}, [deg]') title('(b)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') % phi data for i=1:size(t_ms,1) sensor1.phi(i,1)=atan(sqrt((sensor1.x(i,1)^2)+(sensor1.y(i,1)^2))/(sensor1.z(i,1)))*(182/pi); end subplot(3,1,3) hold on plot(t_ms,sensor1.phi,'color',[1,0.3,0])
104
xlim([0,max(t_ms)]) xlabel('Time, [s]') ylim([-180,180]) ylabel('a_{\phi}, [deg]') title('(b)') box on grid on legend('S_{1}','S_{2}','S_{3}','S_{4}','Location','northeastoutside') %% Max and Min Data disp('x1(min,max); y1(min,max); z1(min,max); r1(max)') disp(['x1(',num2str(min(sensor1.x)),',',num2str(max(sensor1.x)),'); y1(',num2str(min(sensor1.y)),',',num2str(max(sensor1.y)),'); z1(',num2str(min(sensor1.z)),',',num2str(max(sensor1.z)),'); r1(',num2str(max(sensor1.r)),')']) disp('***************') disp('x2(min,max); y2(min,max); z2(min,max); r2(max)') %% Dectct Peaks of r>=1g k(1) = 1; k(2) = 1; k(3) = 1; k(4) = 1; for i=5:size(t_ms,1)-5 if and(gt(sensor1.r(i,1),sensor1.r(i-1,1)),gt(sensor1.r(i,1),sensor1.r(i+1,1))) if ge(sensor1.r(i,1),1) sensor1.r_peaks(k(1),1)=sensor1.r(i,1); k(1) = k(1)+1; end end figure('Name','Boxplot of peaks for r') subplot(1,4,1) boxplot(sensor1.r_peaks,'labels',{'Sensor 1'}) ylabel('ar Peaks, (g)')
105
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BIOGRAPHICAL SKETCH
Muna Jamil Abbas was born and raised in Baghdad, Iraq. She received her
Bachelor of Science in Agricultural Mechanization from college of Agriculture, University
of Baghdad in Iraqi in 1993. In 2004, she received her Master degree in Science in
Agricultural Mechanization from college of Agriculture, University of Baghdad.
Muna received award from the president of University of Baghdad since she
ranked the first over College of Agriculture when she received her Master degree. In
Fall 2010, she attended University of Florida to pursue a doctoral degree at Agricultural
and Biological Engineering department. In 2014, she received her second Master of
Science degree in Agricultural and Biological Engineering, University of Florida. She
was awarded a PhD degree in the Agricultural and Biological Engineering from
University of Florida in 2017.