by-plant prediction of corn grain yield

By-Plant Prediction of Corn (By-Plant Prediction of Corn (Zea Zea mays mays L.) Grain Yield Using Optical L.) Grain Yield Using Optical

Sensor Readings and Measured Sensor Readings and Measured Plant HeightPlant Height

By-Plant Prediction of Corn (By-Plant Prediction of Corn (Zea Zea mays mays L.) Grain Yield Using Optical L.) Grain Yield Using Optical

Sensor Readings and Measured Sensor Readings and Measured Plant HeightPlant Height

K.L. Martin, W.R. Raun, M.L. Stone, J.B. Solie, K.W. K.L. Martin, W.R. Raun, M.L. Stone, J.B. Solie, K.W. Freeman, B. Tubana, B. Chung, R.K. Teal, D.B. Arnall, K. Freeman, B. Tubana, B. Chung, R.K. Teal, D.B. Arnall, K. Desta, S. Moges, C.J. Mack, J.W. Lawless, O. Walsh, S. Desta, S. Moges, C.J. Mack, J.W. Lawless, O. Walsh, S.

Holtz, K. Lawless Holtz, K. Lawless

K.L. Martin, W.R. Raun, M.L. Stone, J.B. Solie, K.W. K.L. Martin, W.R. Raun, M.L. Stone, J.B. Solie, K.W. Freeman, B. Tubana, B. Chung, R.K. Teal, D.B. Arnall, K. Freeman, B. Tubana, B. Chung, R.K. Teal, D.B. Arnall, K. Desta, S. Moges, C.J. Mack, J.W. Lawless, O. Walsh, S. Desta, S. Moges, C.J. Mack, J.W. Lawless, O. Walsh, S.

Holtz, K. Lawless Holtz, K. Lawless

By-Plant Prediction of Corn Grain By-Plant Prediction of Corn Grain YieldYield


This bicycle was modified This bicycle was modified such that a such that a GreenSeeker™ sensor GreenSeeker™ sensor head, power source, and head, power source, and shaft encoder could be shaft encoder could be used to determine used to determine distance for each NDVI distance for each NDVI reading recorded in the reading recorded in the data file.data file.

This bicycle was modified This bicycle was modified such that a such that a GreenSeeker™ sensor GreenSeeker™ sensor head, power source, and head, power source, and shaft encoder could be shaft encoder could be used to determine used to determine distance for each NDVI distance for each NDVI reading recorded in the reading recorded in the data file.data file.

Measure the distance of Measure the distance of each each plant’s locationplant’s location

Calculate the area that Calculate the area that each plant occupieseach plant occupies

Where:Where:DiDi is the area occupied is the area occupied by the iby the ithth plantplantdi-1,di,di+1di-1,di,di+1 are the are the distances to distances to the i-1, i, the i-1, i, and i+1 plantsand i+1 plants

Average NDVI of each plantAverage NDVI of each plant

Measure the distance of Measure the distance of each each plant’s locationplant’s location

Calculate the area that Calculate the area that each plant occupieseach plant occupies

Where:Where:DiDi is the area occupied is the area occupied by the iby the ithth plantplantdi-1,di,di+1di-1,di,di+1 are the are the distances to distances to the i-1, i, the i-1, i, and i+1 plantsand i+1 plants

Average NDVI of each plantAverage NDVI of each plant

2211 iiii

i

ddddD

2211 iiii

i

ddddD

The unit was pushed The unit was pushed along the corn row as along the corn row as illustratedillustrated

White marker plates were White marker plates were used at the beginning used at the beginning and end of each row to and end of each row to designate the start and designate the start and stop pointstop point

The resulting data file The resulting data file included NDVI for each included NDVI for each calibrated distance calibrated distance (approximately 1.1 cm) (approximately 1.1 cm) and sonar height (when and sonar height (when sonar was used)sonar was used)

The unit was pushed The unit was pushed along the corn row as along the corn row as illustratedillustrated

White marker plates were White marker plates were used at the beginning used at the beginning and end of each row to and end of each row to designate the start and designate the start and stop pointstop point

The resulting data file The resulting data file included NDVI for each included NDVI for each calibrated distance calibrated distance (approximately 1.1 cm) (approximately 1.1 cm) and sonar height (when and sonar height (when sonar was used)sonar was used)

Data PresentationData PresentationData PresentationData Presentation

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Distance (cm)

ND

VI (R

aw)

Pass #1 Pass #2 Plant

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Distance (cm)

ND

VI (P

lant

Ave

rage

)

Individual data points collected every 1.1 cmIndividual data points collected every 1.1 cm

Average NDVI for each plantAverage NDVI for each plant

NDVI vs. Grain Yield at all sites NDVI vs. Grain Yield at all sites (2003 and 2004)(2003 and 2004)NDVI vs. Grain Yield at all sites NDVI vs. Grain Yield at all sites (2003 and 2004)(2003 and 2004)

Grain Yield = 2021.5e91.918 * INSEY

R2 = 0.29

0

5000

10000

15000

20000

25000

0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02INSEY

Gra

in Y

ield

(kg

ha

-1)

EFAW , OK (2003)

LCB, OK (2003)

EFAW , OK (2004)

Shelton, NE (2004)

Ames, IA (2004)

LCB, OK (2004)

Hennessey, OK (2004)

INSEY vs. Grain Yield at all sites INSEY vs. Grain Yield at all sites where height was recordedwhere height was recordedINSEY vs. Grain Yield at all sites INSEY vs. Grain Yield at all sites where height was recordedwhere height was recorded

Grain Yield = 2193.6e82.918 * INSEY

R2 = 0.27

0

5000

10000

15000

20000

25000

0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02INSEY

Gra

in Y

ield

(kg

ha

-1)

EFAW , OK (2004)

LCB, OK (2004)


Do these plants compete with one another?

If they do, what above ground parameter(s) can we use to evaluate this?

Does the proximity of the competitor have an impact on the level of competition?

Do these plants compete with one another?

If they do, what above ground parameter(s) can we use to evaluate this?

Does the proximity of the competitor have an impact on the level of competition?

1 2 3 4 5

Plant Competition EquationPlant Competition EquationPlant Competition EquationPlant Competition Equation

pq-2 pq-1 pq Pq+1 Pq+2

2/)( 2

1

pqpq

pq

HtHt

Ht

2/)( 2

1

pqpq

pq

HtHt

Ht

2/

2/)(2/)( 2

1

2

1

pqpq

pq

pqpq

pq

pq

HtHt

Ht

HtHt

Ht

Ht

Cadj of the Plant in Question

In a series of five plants, the height of plant number 2 and 4 is compared to the average of the height of their neighbors to assess the competitive ability of plants 2 and 4 as compared to the plant in question, plant 3. This value generated from the first step of this process (weighted average value of plant 2 and 4 in the series) is then compared to the actual height of plant 3 to result in a weighted comparison of the competitive ability of plant 3 to its neighbors

In a series of five plants, the height of plant number 2 and 4 is compared to the average of the height of their neighbors to assess the competitive ability of plants 2 and 4 as compared to the plant in question, plant 3. This value generated from the first step of this process (weighted average value of plant 2 and 4 in the series) is then compared to the actual height of plant 3 to result in a weighted comparison of the competitive ability of plant 3 to its neighbors

INSEYC D

adj estGY GYest is the estimated grain yield Cadj is the competition adjustment

factor D is the linear distance occupied by

each plant INSEY is the in-season estimate of

yield

GYest is the estimated grain yield Cadj is the competition adjustment

factor D is the linear distance occupied by

each plant INSEY is the in-season estimate of

yield

The competition adjustment factor accounts for the competitive ability of the plant in question when considering its height against those that surround it. This value is then divided by the linear distance to adjust the index due to the change in proximity of the competitive plants (a closer proximity to the competitive plant will divide by a smaller number resulting in more emphasis being placed on the height comparison). The resulting value is then multiplied by INSEY to allow for an incorporation of biomass produced per day.

The competition adjustment factor accounts for the competitive ability of the plant in question when considering its height against those that surround it. This value is then divided by the linear distance to adjust the index due to the change in proximity of the competitive plants (a closer proximity to the competitive plant will divide by a smaller number resulting in more emphasis being placed on the height comparison). The resulting value is then multiplied by INSEY to allow for an incorporation of biomass produced per day.



Yield Prediction Using INSEYYield Prediction Using INSEY

Yield Prediction Using GYest

Yield Prediction Using GYest

Grain Yield = 538596*INSEY+939.9

R2 = 0.16

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0 0.005 0.01 0.015 0.02 0.025INSEY

Gra

in Y

ield

(kg

ha-1

)

EFAW, OK (2004)

LCB, OK (2004)


EFAW, OK (2005)

LCB, OK (2005)

Grain Yield = 15661 * GYest + 3367.2R2 =0.48

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8GYest

Gra

in Y

ield

(kg

ha-1

)

EFAW, OK (2004)

LCB, OK (2004)


EFAW, OK (2005)

LCB, OK (2005)

Questions??Questions??

by-plant prediction of corn grain yield

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