field and weather monitoring with youths as sensors for
TRANSCRIPT
65
Available online at www.jstage.jst.go.jp/
Agricultural Information Research 21(3), 2012. 65–75
Original Paper
Field and Weather Monitoring with Youths as
Sensors for Agricultural Decision Support
Takashi Togami1), Seishi Ninomiya2), Kyosuke Yamamoto2), Yumiko Mori3), Toshiyuki Takasaki3),
Yasukazu Okano3), Ryoichi Ikeda4), Akane Takezaki5) and Takaharu Kameoka*1)
1) Graduate School of Bioresources, Mie University, 1577 Kurimamachiya, Tsu, Mie 514-8507, Japan
2) Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
3) NPO Pangaea, Shijo Hirano Bldg. #402, 716-1 Shin Kamanza, Shimogyo, Kyoto 600-8471, Japan
4) Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, 1-1-1 Sakuragaoka, Setagaya,
Tokyo 156-8502, Japan
5) National Agricultural Research Center, National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba,
Ibaraki 305-8517, Japan
Abstract
In Vietnam, proper and continual agricultural engineering guidance for local farmers is required to improve rice cultivationand conserve environment, yet there are issues in the low literacy rate of adults and information dissemination. In order toresolve the issues, the YMC-Viet project, based on the Youth Mediated Communication (YMC) model in which youthsmediate communication between local farmers and agricultural experts in remote locations, was proposed. It is inadequateonly to unilaterally extract problems from farmers and provide guidance. Advice can be further optimized by providing localquantitative data to agricultural experts. Accordingly, information collection to accurately comprehend local environmentalconditions and the rice growth situation is indispensable. However, there is no weather station in the target area andenvironmental information is severely lacking. In addition, the number of agricultural experts in the area is extremely lowand gaining an understanding of the field situation is almost impossible. In this research, therefore, we devised a method toregularly collect environmental and field situation information, by having youths working as weather and field sensors, andutilizing the collected data for agricultural decisions supported by agricultural experts in remote locations, and its efficacywas verified. Then, we created a cultivation knowledge resource and applied it as a contrivance for youths to adequatelyinform agricultural experts about field growth situations based on collected information. As a result of the experiment, it wasrevealed that youths met more than 85 percent of the requirements of temperature and humidity measurement and weatherobservation. Additionally, the data collected and the records showed higher potential by agricultural experts for utilizing thedata in agricultural decision support. Moreover, it was confirmed that sometimes youths functioned as disorder and defectdetectors. Thus, the efficacy of youths as sensors in terms of collecting information for agricultural support was verified.
Keywords
youths as sensors, agricultural decision support, information collection, cultivation knowledge resource, YMC Model
Introduction
Agriculture is the basic industry in Vietnam and agricultural
produce, especially rice as their principle product, is necessary to
improve competitiveness in the international market. In order to
achieve it, stably produced high quality rice is required.
Until now, the yield amount has been secured, yet the level of
farmers’ rice cultivation technique in the country is low and the
overall product quality is extremely low. Moreover, there are
issues of cost and environmental conservation due to high density
planting and excess fertilization. Hence, the Vietnamese govern-
ment has been promoting a shift in policy to one of resource and
environmental conservation (Hung 2008).* Corresponding Author
E-mail: [email protected]
Field and Weather Monitoring with Youths as Sensors for Agricultural Decision Support
66
However, degradation in land capability caused by excess fer-
tilization has been observed, with the cause of fertilization being
local farmers’ unquestioning belief, from ignorance, that fertiliza-
tion directly leads to a secure yield. As a consequence, concern
about the stability of farming has been rising. Thus, the imple-
mentation of appropriate and continual technical guidance for
farmers is necessary.
In recent years, studies aiming to accurately disseminate infor-
mation on a sophisticated agricultural technique have been
widely conducted. For instance, Reddy and Ankaiah (2005)
proposed a cost effective agricultural information dissemination
system (AgrIDS) with the purpose of delivering expert agricul-
tural knowledge to farming communities in India in order to
increase crop yields. Another example is the research by Ratnam
et al. (2006), which describes the dissemination of customized
information to farmers using an IT based personalized agricul-
tural extension system (eSagu). Similarly, Armstrong and
Diepeveen (2008) described the results of a case study of the
Farmer Decision Support Framework (FDSF). Additionally in
Japan, Kamiya et al. (2011) developed the web-based interface
for sharing cultivation information among local communities.
However, there was a problem with information dissemination
in a rural area of Vietnam due to the illiteracy issues of adults.
In order to solve the problem, the YMC-Viet project was pro-
posed (Mori et al. 2011). In this project, the YMC model (Mori
2009) was applied and farmers and agricultural experts, with the
mediation of educated youths, communicated with each other.
However, it is inadequate only to unilaterally extract problems
from farmers and provide guidance. Further optimization of the
advice should be carried out by providing local quantitative data
to agricultural experts in remote locations.
In order to do this, information collection to accurately assess
local environmental conditions and the rice growth situation is
indispensable. In this regard, nowadays, research using an envi-
ronmental monitoring device and a sensor network has widely
been conducted (Wang et al. 2006, Ruiz-Garcia et al. 2009). We
have also worked on research into agro-environmental monitor-
ing in terms of utilizing data on olive cultivation (Togami et al.
2010), research using a wireless sensor network in a mandarin
orange orchard (Togami et al. 2011a) and in a vineyard for smart
viticulture management (Togami et al. 2011b). However, it is
difficult to install monitoring devices in Vietnam in local areas
where power supplies and network infrastructures are undevel-
oped.
Utilizing middleware, such as MetBroker (Laurenson et al.
2002), which accesses many different weather databases and
provides weather data, is a possible solution, yet there is no avail-
able weather station in the area and environmental information is
severely lacking. In addition, the number of agricultural experts
in the area is extremely low and gaining an understanding of the
field situation is almost impossible.
In this research therefore, we devised a method whereby
youths worked as weather and field sensors, regularly collecting
environmental and field growth situation information, with
agricultural experts utilizing the information from the youths for
agricultural decision support, and we attempted to comprehend
the feasibility of data collection and the efficacy of information
exchange facility by youths as sensors. Then, we created a culti-
vation knowledge resource and applied it as a contrivance for
youths to adequately inform agricultural experts in remote loca-
tions about field and growth situations based on collected infor-
mation.
Materials and Method
Details of demonstration experiment
In this research, the target area is Thiê.n My~, Trà Ôn District,
Vinh Long, Vietnam, which is about 30 square kilometers shown
in Fig. 1.
Before carrying out this research, the hearing survey to 30
farmhouses was conducted. Table 1 shows a part of the results of
the survey and it presents typical 5 of 30 farmhouse answers.
According to the survey, minimum acreage is 0.15 hectares while
maximum acreage is 2.2 hectares, and the average is 0.57 hec-
tares. In regard to the approximate distance between paddy and
house where youth observes the weather, minimum distance is
about 120 meters, maximum distance is about 2,800 meters and
the average is 607 meters. Cultivar varies among farmhouses
Fig. 1 Location map of Thiê.
n My~, Trà Ôn District, Vinh Long,Vietnam (Map obtained from Google Maps <http://maps.google.com/maps>)
Agricultural Information Research 21(3),2012
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and OM 4900, OM 4218, Jasmine 85, IR 50404 and OM 1490 are
cultivated.
After the survey, 30 youths from 29 farmhouses of the 30
farmhouses were selected and their ages are from 11 to 15. In
addition, their parents have low literacy levels.
The official project period is from mid-February to the end of
March in 2011, and the period falls under the growth stage
between seedling stage and meiotic stage.
Determination of measurement components, instruments and
intervals
In order to have youths working as weather and field sensors, it
is critically important to set minimum yet effective measurement
components and numbers of measurements which do not become
a burden on the youths. Otherwise, it is likely that the youths will
abandon the role and not function as sensors.
Hence, an agricultural group, consisting of agricultural experts
and researchers, an agricultural terminology researcher, a child
education expert and a systems architecture expert, was formed
to determine the measurement components, instruments and
numbers of measurements necessary to interpret the crop growth
situation, crop conditions and field conditions, and to provide
advice on the next action.
Table 2 summarizes the measurement components, instru-
ments and numbers of measurements. Five measurement compo-
nents were set: temperature and humidity, weather, crop height
and leaf color. In addition, two tasks were also given to youths to
comprehend field situations. One is the observation and counting
of insects and the other the digital image acquisition of rice
leaves, rice stems and any defects in the paddy field. The relation
and necessity of each component to rice cultivation follow.
Temperature and humidity
Air temperature and humidity induce the sterility of rice. For
example, Matsui et al. (1997) described the relation between
the spikelet sterility of Japonica rice at flowering and air temper-
ature, humidity and wind velocity conditions. According to
Weerakoon et al. (2008), in tropical rice-growing ecosystems,
high temperature-induced grain sterility in rice became a serious
problem. Therefore, it is important to measure temperature and
humidity in the target area.
Weather
Climate conditions influence rice growth. Kawatsu et al.
(2007) reported changes in weather conditions and their effects
on rice production using a data set covering 40 years in Japan.
Thus, weather observation in the target area should be imple-
mented for stable rice production.
Crop height
For the growth diagnosis of rice plants it is necessary to meas-
ure the height of the rice. For example, elongated seedlings
appear to be caused by Bakanae disease (Naito et al. 2008).
Hence, even in Vietnam, it is assumed that measurement of crop
height is important to detect an affected rice plant earlier and to
monitor rice growth.
Leaf color
Leaf color is one of the most important indicators for the
growth diagnosis of rice plants. Islam et al. (2007) presented the
research results for nitrogen use efficiency in rice using rice leaf
color charts. Additionally, a lesion also appears on rice plants and
it is easily recognized by its color. Even though there are a lot of
cultivars grown in the target area, leaf color can be used to diag-
nose a rice plant.
Insect count
The amount of insect pests emerging varies yearly and season-
ally in each field, and the variance relates to the stage in rice
growth and the fertilization system (Inoue and Fukamachi 1990,
Miyashita and Kawanishi 2003). In addition, insect pests influ-
ence rice quality and the volume of production. For instance,
Miyashita (1985) reported the relation between the amount of
production and injury to rice leaves by rice leafholder at two dif-
ferent growth stages. Thus, although location and fertilization
system of target area is different from above research reports, it
can be thought that insect count is crucial for appropriate pest
control.
Table 1 Result of hearing survey: Farmhouse description
No.Youth
ID
Acreage
(ha)
Approximate
Distance between
House and Paddy (m)
Cultivar
3 3 2.2 240 Jasmine 85, OM
4900, OM 4218
4 4 0.4 350 IR 50404, OM 4900
5 5 0.5 470 OM 4900, OM 1490,
IR 50404
20 20 0.4 1,060 OM 1490, Jasmine 85
26 26 0.5 180 OM 4900
The table above presents typical 5 of 30 hearing survey results.
Table 2 Measurement components, instruments and intervals
Measurement
Components
Measurement
Instruments
Measurement
Intervals
Temperature and Humidity Thermo-hygrometerDaily
Weather Kids’ Eye
Crop Height MeasureTwice weekly
Leaf Color Leaf Color Chart
Field and Weather Monitoring with Youths as Sensors for Agricultural Decision Support
68
Digital photographs
Digital photographs help the youths communicate properly
with the agricultural experts and also help the experts diagnose
rice plant conditions and provide action advice. Therefore, digital
photographs of rice plants can be a strong communication tool.
There are two different intervals of measurement determined in
this research. One is daily measurement and the other is twice
weekly measurement. The components of daily measurement
comprise a collection of data that becomes the backbone for
scientifically interpreting crop growth and condition, and are
necessary for the later use of analysis and development of crop
growth simulation. Twice weekly measurement components
were set for the purpose of assessing the changes in crop growth
and field environment over the seasons.
Data collection method
In order to support youths for data collection, a specialized
booklet called YMC passport was created by NPO Pangaea
shown in Fig. 2a, and provided to each youth. This booklet con-
sists of several components such as notebook for the weather
observation (Fig. 2b), notebook for questions from parents to
agricultural experts and the answers (Fig. 2c), measurement
instructions (Fig. 2d) and insect pest reference (Fig. 2e). All
youths use YMC passport during the project.
There are two data collection flows corresponding to daily and
twice weekly measurements. Daily, youths read the temperature
and humidity values from a thermo-hygrometer (Fig. 3a) placed
outside of each house and observe the weather; then, they record
them in the YMC passport and subsequently, they send the
recorded data to the YMC system’s server using mobile phones,
as shown in Fig. 3b. Twice weekly, the youths go to their respec-
tive rice paddies and implement four tasks. First, they measure
the height of the rice with the tape measure provided (Fig. 3c).
Second, they check the rice leaf color against a leaf color chart
Fig. 2 YMC passport: a) Front and back cover, b) Notebook for observation, c) Notebook for question and answer, d) Measurementinstruction, e) Insect pest reference
Fig. 3 Measurement tools: a) Thermo-hygrometer, b) Mobile phone, c) Tape measure, d) Leaf color chart, e) Bug search board
Agricultural Information Research 21(3),2012
69
(Fig. 3d) and record the closest numbered color on the chart.
Then they record both the rice height and leaf color results in the
YMC passport. Third, they check the insects with a bug search
board (Fig. 3e), for the purpose of pest control, by holding the
board in one hand and beating the rice plant with other and then
counting the fallen insects. If the number is greater than 15, they
take a digital photograph with the mobile phone provided, which
has a camera with a resolution of 1.3 mega pixels, and send the
photographs, with a report, to an agricultural expert since 15
insects on one rice plant can be interpreted as an extraordinary
situation. Lastly, they take digital photographs if there is any
defect on the rice or in the paddy.
Normally, repeated measurement is necessary for measure-
ments. In addition, it is necessary to measure crop height of
several rice plants and use the average value as the crop height.
In this research however, the observation and measurements are
carried out by youths. Hence, it is critically important to minimize
the burden on youth to make them function as sensors, which is
mentioned in earlier section. Thus, the number of determinants
for all measurements was set to one. In regard to the time of
measurement for daily measurement and weather observation, it
was not definitely set since youths go to school. Therefore, it was
set to before going to school as much as possible.
In order to minimize errors in measurement and data collection
under the difficult situation as mentioned above, a briefing
session and a training session for the use of measurement tools
and for measurement were held before this research. Then, with
regard to crop height, youths were instructed to measure the
ground from the highest leaf or the highest ear of a rice plant.
Creation of cultivation knowledge resources
Cultivation knowledge resources were created with two pur-
poses; one, in order for youths to adequately inform agricultural
experts in remote locations about the field and growth situations
based on collected weather and field information, and the other,
so that agricultural experts can give optimal advice and properly
convey the information.
With regard to proper information exchange, it was essential to
prepare knowledge resources in Multilanguage in this project.
Accordingly, cultivation knowledge resources were prepared in
three languages; Japanese, English and Vietnamese. Then, the
translation was supported by the Language Grid (Ishida 2011),
which provides multilingual communication support and is an
online multilingual service platform.
Fig. 4 shows the creation flow for the cultivation knowledge
resources. At first, categorization of the components in relation to
rice cultivation, such as plant physiology, rice paddy and irriga-
tion, was implemented among the agricultural group in order to
organize rice cultivation knowledge. In the categorization, main
categories, subcategories, which relate to each main category, and
sub-subcategories, relating to each subcategory, were created.
Next, in accordance with the main categories, drafts of the cul-
Fig. 4 Cultivation knowledge creation flow
Field and Weather Monitoring with Youths as Sensors for Agricultural Decision Support
70
tivation knowledge resources for experts’ use were created based
on specialized books, research papers, articles and web materials.
After that, for ease of understanding and to create correct
knowledge, the drafts were checked and corrected by agricultural
group members. Then, they were accumulated as the knowledge
resource. After the first check of drafts by the experts, cultivation
knowledge for the youths’ use was created as a parallel text
corresponding to the corrected drafts.
Results
Functionality of youths as sensors
During the official project period, each youth was to conduct
47 daily measurements and 13 twice weekly measurements.
Table 3 summarizes the result of the fundamental statistics for
each task.
For the daily task, on average, 41 measurements were con-
ducted, and the median, larger than the mean, and the negative
value of the skewness indicate that each frequency distribution
curve is skewed to the right. On the other hand, one youth
performed only 20 daily measurements, which is a 43 percent
satisfaction rate.
From the statistical results of the twice weekly task, it was
revealed that the youths, on average, conducted 11 measure-
ments, which is about an 85 percent satisfaction rate, but one
youth implemented only about 62 percent of the required tasks.
In addition, both medians and the skewness reflect that each
frequency distribution curve is skewed to the right.
With regard to the photographs, the number of updates indi-
cates the number of uploads while the number of images indicates
the number of digital photographs taken by each youth. This task
was at the youths’ discretion and youths took photographs at their
paddy fields whenever they detected any defects. Therefore, the
maximum number of updates corresponds to the maximum num-
ber of twice weekly measurements. The results showed that both
the numbers of updates and images varied widely among the
youths from 2 to 13 updates and 6 to 196 images.
Data collected by youths
Temperature and humidity data varied quite widely among the
youths. Table 4 shows an example of the results for temperature
and humidity data recorded by youths on two days. The tempera-
ture data on February 13th were quite uniform, except for two
instances, while the temperature data on March 18th varied
greatly from 27 up to 35 degrees Celsius. In addition, obvious
human error, such as the temperature and humidity data of youths
ID 10 and 23, could be noted from the results.
The results of the weather observations differed even at the
same time of the same day. Table 5 shows an example of the
results of the weather observations by youths in chronological
order. The time in the table corresponds to the update time of the
data. Observation records between youth ID 28 and 3 on March
17th are within a minute, yet the records show different weather
even though the distance between each house is about 720 meters
according to the result of previous hearing survey. On the day,
Youth ID 28 recorded 31 degrees Celsius with the humidity of
Table 3 Statistical results for each youth task
Daily Task Twice Weekly Task Degital Photographs
Temperature Humidity Weather Rice Height Leaf Color No. Update No. Images
No. of Sample (Persons) 30 30 30 30 30 30 30
Mean (No. of times) 41.7 41.7 41.6 11.1 11.1 8.4 57.9
Median (No. of times) 43.5 43.5 44 11.5 11.5 9 50
Mode (No. of times) 46 46 46 12 12 10 55
Standard Deviation: U 5.7 5.7 5.7 1.4 1.4 3.3 44.9
Unbiased Estimator of Variance: U2 32.8 32.8 32.5 1.9 1.9 10.7 2012.8
Skewness –2.1 –2.1 –2.1 –0.6 –0.6 –0.4 1.6
Max (No. of times) 47 47 47 13 13 13 196
Min (No. of times) 20 20 20 8 8 2 6
Range 27 27 27 5 5 11 190
Table 4 Temperature—humidity data measured by youths
Date MeasurementYouth ID
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Feb. 13th Temperature (°C) 28 28 28 28 28 28 28 28 28 28 30 28 33 28 29 28
Humidity (%) 70 70 68 67 70 67 68 68 70 68 68 70 48 70 69 70
Mar. 18th Temperature (°C) 70 34 28 — — 30 30 — 35 27 33 30 29 72 29 28
Humidity (%) 50 60 72 — — 70 70 — 60 70 69 76 72 27 70 76
— indicates no data
Agricultural Information Research 21(3),2012
71
70 percent while youth ID 3 recorded 30 degrees Celsius with the
humidity of 70 percent. Additionally, when extending the time
span to 10 minutes duration, observation records of youths ID 12
and 28 on March 17th, and youth ID 11 and 28 on March 18th
are completely different, one is sunny and the other is rainy.
The result of rice height measurement records showed great
variation among the youths. Fig. 5a and 5b show rice growth sit-
uations during the project period. The rice height of the vertical
axis represents the highest leaf or ear of rice plant in a rice plant.
The numbers on the horizontal axes such as “1-1” indicates the
week number and the measurement number. For instance, “1-1”
indicates the first measurement in week 1. From Fig. 5a it can be
seen that the rice grew steadily over time. On the other hand, Fig.
5b shows a completely different result. Rice plants of youth ID 6,
12 and 15 became smaller in height. The decrease, in the case of
youth ID 15, is especially dramatic. In addition, youth ID 12 and
15 started recording the height of a rice plant which had already
grown substantially. Furthermore, deficits in the data came to the
fore.
With regard to the result of leaf color, observation records were
similar to the result of rice height measurement. The records show
the change in color and the color change indicate youths recorded
the data by using the color chart. However, results that suggest the
necessity of tutorial for proper use of color chart were obtained.
There were a total of 1737 digital photographs taken by the
youths, and they could be divided broadly into nine categories in
relation to rice cultivation. The categories are insect, leaf, paddy
field, rice ear, rice height, rice plant (the entire body imagery),
rice stem, root and weed. Subsequently, picture quality was deter-
mined. About 41 percent of the photographs (704 photographs)
were blurred. In addition, the proportion of clear photographs in
which the target could not be judged or could not be identified by
a third person due to the size of the target being too small size was
approximately 30 percent. Furthermore, there were two sizes of
photograph: 176 by 220 pixels, and 1280 by 1024 pixels. This
small size made the judgment more difficult for a third person. On
the other hand, the results affirmed the usefulness of images.
Fig. 6 shows examples of photographs taken by the youths and
Table 5 Weather observation by youths
Youth IDMarch 17th March 18th
Time Weather* Youth ID Time Weather*
Youth 5 10:05:56 2 Youth 11 11:00:15 1
Youth 8 10:08:22 3 Youth 28 11:07:18 4
Youth 30 10:15:14 2 Youth 24 11:11:30 2
Youth 4 10:32:09 3 Youth 19 11:17:56 3
Youth 11 10:34:15 1 Youth 27 11:18:21 3
Youth 12 10:50:32 1 Youth 30 11:23:49 4
Youth 28 10:58:12 4 Youth 21 11:34:10 2
Youth 3 10:58:40 2 Youth 22 11:39:28 2
Youth 24 11:00:27 4 Youth 6 11:52:43 4
Youth 27 11:16:42 2 Youth 23 12:05:49 3
Youth 21 11:39:57 2 Youth 26 12:17:02 4
Youth 1 11:41:10 2 Youth 18 13:09:15 1
* Weather data where 1: Sunny, 2: Partly Cloudy, 3: Cloudy, 4: Rainy
Fig. 5 Rice growth situation using rice height records: a) Intelligible growth situation, b) Unintelligible growth situation
Field and Weather Monitoring with Youths as Sensors for Agricultural Decision Support
72
the target of each picture could be easily recognized and catego-
rized. For instance, Fig. 6a can be recognized as eggs, Fig. 6b as
an insect larva, Fig. 6c as an infected leaf and Fig. 6d as cracks in
a paddy field.
Cultivation knowledge resources
As a result of the categorization, nine main categories related
to rice cultivation were created, together with 57 subcategories
corresponding to the main categories, and 108 sub-subcategories
according to the subcategories,.
In the creation of cultivation knowledge resources, over 1,400
resources for the youths’ use based on environmental information
and field growth situation were created. Additionally, more than
900 resources for experts’ use to provide optimal advice were
created in order to properly convey sophisticated agricultural
techniques to the youths with different language backgrounds.
Table 6 shows an example of the created cultivation knowledge
resources. In the communication between youths and agricultural
experts, youths select question from knowledge resources for
youths on the web according to the question from parents or ques-
tion based on their paddy and rice growth situation, and then send
the message with digital image if necessary to the experts. The
experts then check the measured data of the youths. After that
they create an answer and next action advice by combining the
knowledge resource for experts, and then send it back to the
youths. After that, youths check the answer and convey the
answer to parents. Parents then use the answer for decision-
making. In total, the youths and agricultural experts communi-
cated with each other 260 times using the resources.
Discussion
In this section, the youths’ capability in data collection is dis-
cussed according to the results. First, the fundamental statistics of
both the daily and twice weekly tasks suggest that youths are
capable of undertaking long-term data collection. Therefore,
using youths as sensors will contribute to meteorological obser-
vation where it is difficult to construct a weather station and
where meteorological data is not available.
Second, rice height measurement and clearly acquired images
indicate the high potential of utilizing the collected data for
proper agricultural management. For instance, Fig. 6a can be
easily recognized as the eggs of an apple snail (Pomacea
canaliculata), which is well known as a serious pest threatening
rice production. Accordingly, agricultural experts will be able to
provide proper guidelines for prevention or removal methods.
In addition, the photography task revealed that the youths under-
took three functional roles as field sensors: field monitoring,
defect detection and informing. Consequently, it is assumed that
earlier detection of disease and defects in each paddy field will be
possible.
Last, it was assumed that, as a consequence of creating cultiva-
tion knowledge resources, the youths were able to communicate
with the agricultural experts about wide-ranging topics based on
Fig. 6 Digital photographs taken by youths: a) Eggs, b) Insect larva, c) Infected leaf, d) Cracks in a paddy field
Table 6 Examples of created cultivation knowledge resources
Knowledge Resources for Experts Knowledge Resources for Youths
There are various types of insect pests damaging rice plants. A plant-hopper and a
leafhopper suck out plant juice, and pass on the virus causing a viral disease.
Additionally, locusts can be found around a paddy during the harvest season. Even if you
store rice grains after harvest, a maize weevil eats away the grains.
Is there any insect harmful for a rice plant?
Ensure fertilizer is stored in a building. Sunlight makes the fertilizer bag fragile. When
fertilizer gets wet with rain, it turns into a solid mass. Please cover the fertilizer with an
opaque sheet so as to protect it from sunlight and rain when putting it outside. Fertilizer
should not be placed directly on the ground.
How do I store fertilizer? Can I store it outside?
Please look at the shape of the reseda. Is it longitudinally diamond-shaped? If so, it
indicates rice blast.
Reseda and bistred mottles are on a rice leaf. What are they?
Agricultural Information Research 21(3),2012
73
collected weather and field information.
On the other hand, several assignments have been extracted
from the results. First, the variability of the measured data and
observation records clarified the importance and necessity of
defining clear measurement rules before employing youths with-
out agricultural knowledge as field sensors so that the data and
the records can be used for later analysis.
Second, the failures in measurement, such as rice height
measurements showing a decrease in height with rice growth,
leaf color and photography failures, such as blurred photographs,
clarified that a minimum agricultural education, including proper
measurement methods and a tutorial for proper use of data col-
lecting tools, are the keys for data collection by youths as
sensors. In addition, in terms of utilizing photographs effectively
in agricultural management, having digital photographs without
recognizable targets revealed the need for metadata explaining
each picture.
Last, the feedback from the youths and their parents, such as
requests for the replenishment of resources in a specific category
and the incidence of the lack of a category, made it necessary for
them to consult with the agricultural experts. Therefore, the user’s
requests regarding the cultivation knowledge resources should be
heard and carried out in order to enrich the resources in accor-
dance with the research.
Thus, the feasibility for data collection and the efficacy for
information exchange facility of the youths as sensors were
demonstrated in terms of continuously collecting information on
the weather and field growth situations on a long-term basis. In
addition, many advantages of using the youths as sensors were
clarified, such as low cost, maintenance free, applicability and
movability to any measuring point, and the simplicity of earlier
disorder and defect detection. Among the advantages, the main
advantage would be the possibility of spontaneous improvement
in measurement accuracy and the speed of defect detection, with
an increase in both the agricultural and general knowledge of the
youths. Hereafter, the establishment of clearly defined measure-
ment rules and the development of tools for defect detection such
as silicon balls developed by Suzaki et al. (2011) are necessary to
maximize the advantages for the youths, and for the experts to
effectively utilize the collected information in agricultural deci-
sion support. In addition, the arrangement of educational pro-
grams which correspond to both agriculture and the measurement
tools, and the regular verification of data during cultivation, are
also significant future tasks.
At this moment, a system for utilizing the collected data has not
been reached, yet weather observation by many youths in areas
where there are no weather stations is significant and higher
measurement accuracy can be expected. In the near future, if
youths can act as sensors, not only will conveying information to
agricultural experts in remote locations and optimizing advice
become possible, but also the development of rice growth simula-
tion models, tailored to a target field by continual information
collection, will be possible. Accordingly, proper cultivation
management, such as fertilization based on accurate forecasts for
the sprouting season for ears of grain, can be implemented.
Moreover, the roles of youths as weather and field sensors and
the communication between youths and agricultural experts
enable sophisticated agricultural techniques to be conveyed to the
youths. In addition, the role and the communication can also
make the youths realize the importance of agriculture, and it will
be possible to foster youths as future farmers in local areas and as
future technical engineers. From farmers’ or youths’ perspective,
there are significant advantages of our proposed system. First, it
is possible to obtain optimal action advice according to a paddy
situation, growth stage and cultivar from experts. Second, if field
and weather data are collected and accumulated, the data will
become reference data for next cultivation. Last, they are able to
acquire proper knowledge, and the production of high quality rice
would be possible by implementing proper cultivation manage-
ment that includes appropriate usage of agricultural materials
such as fertilizer and agricultural chemicals, leading to the
improvement in profitability.
Conclusion
This paper devised the method of having youths working as
weather and field sensors in an area where the adult literacy rate
is low and environmental information is severely lacking, and to
regularly collect information on the environment and field growth
situations. Furthermore, utilization of collected data by agricul-
tural experts in remote locations was also devised.
The fundamental statistics for the number of determinations of
each measurement component revealed that long-term data col-
lection by youths as sensors would be highly possible. In addi-
tion, properly measured rice height records and clearly taken
pictures indicated that the collected data would be applicable to
agricultural experts providing optimal advice. In consequence of
creating cultivation knowledge resources as a contrivance to
adequately inform agricultural experts of the local situation, it
was ascertained that the youths utilized the resources and commu-
nicated with the experts many times. Thus, the feasibility of data
collection and the efficacy of information exchange facility of
youths as sensors were verified.
On the other hand, several issues to improve the functionality
of youths as sensors were also obtained. From the variability in
measured data and observation records, it was clarified that
clearly defined measurement rules are imperative before employ-
ing youths as field sensors. Additionally, the measurement and
photography failures suggested the significance of and need for
educational programs in both agriculture and the proper use of
Field and Weather Monitoring with Youths as Sensors for Agricultural Decision Support
74
measurement tools and devices. Therefore, the establishment of
clearly defined measurement rules and the arrangement of educa-
tional programs in both agriculture and measurement tools are
the next important tasks in order to improve the functionality of
youths as sensors. Additionally, the replenishment of the agri-
cultural cultivation resources, including category addition, is a
further task necessary to achieve more accurate information
dissemination.
In this research, data collected by the youths has not yet been
utilized as a system. In terms of the sophistication of agricultural
engineering guidance therefore, the utilization of field data
collected by youths is the main challenge. In the near feature, if
the development of a rice growth simulation model, tailored to a
target field by the combination of digital photographs of different
growth stages and rice height records, becomes possible, it can
be utilized in the guidance to indicate the proper time for rice ear
fertilization and harvesting. In addition, the model can also be
expected to apply the observation records of the leaf color and
occurrence of insect pest, and digital photographs showing the
infected rice plant into the cues for the proper timing of pesticide
spraying and fertilizing. From farmers’ or youths’ perspective,
there are significant advantages of our proposed system. First, it
is possible to obtain optimal action advice according to a paddy
situation, growth stage and cultivar from experts. Second, if field
and weather data are collected and accumulated, the data will
become reference data for next cultivation. Last, they are able to
acquire proper knowledge, and the production of high quality rice
would be possible by implementing proper cultivation manage-
ment that includes appropriate usage of agricultural materials
such as fertilizer and agricultural chemicals, leading to the
improvement in profitability.
Acknowledgement
This project was funded by the Ministry of Internal Affairs and
Communications as part of its emphasis on Information and Com-
munication Technology model projects in three priority areas in
developing countries (Ubiquitous Alliance Project). The authors
would like to thank Prof. Toru Ishida of Kyoto University for
advice and support.
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Received April 6, 2012
Accepted June 11, 2012
Ergonomics & Outreaches