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UAS-based imaging for prediction of chickpea crop biophysical parameters and yield
Year:
2023
Authors :
Bonfil, David J.
;
.
Lati, Ran
;
.
Volume :
205
Co-Authors:

Asaf Avneri
Shlomi Aharon
Anna Brook
Guy Atsmon
Evgeny Smirnov
Roy Sadeh
Shahal Abbo
Zvi Peleg
Ittai Herrmann
David J. Bonfil
Ran Nisim Lati

Facilitators :
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0
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Total pages:
1
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Abstract:

Chickpea (Cicer arietinum L.) is a key legume crop grown in many semi-arid areas. Traditionally, chickpea is a rainfed spring crop, but in certain countries it has become an irrigated crop. The main objective of this study was to evaluate the ability of Unmanned Aerial Systems (UAS) imaging platform with an integrated RGB camera to provide estimations of leaf area index (LAI), biomass, and yield for chickpea during the irrigation period. Two field trials were conducted in 2019 and 2020, in which chickpea plants were subjected to five and six irrigation regimes, respectively. Eight vegetation indexes (VIs) and three morphological parameters were estimated from the RGB images. In parallel, biomass was determined, LAI was measured manually, and yield was determined at full maturity. In total, 294 plant samples were acquired and analyzed over the two years. Firstly, each of the VIs and morphological parameters were correlated separately against the two biophysical parameters and yield. Then, all the VIs and morphological parameters were analyzed together, and two statistical models, partial least squares regression (PLS-R) and support vector machine (SVM); were used to predict biomass and LAI. The yield was predicted using multi-linear regression (MLR). When each index or morphological parameter was analyzed separately, plant height and some of the VIs provided adequate predictions of the biophysical parameters in 2019 (R2 values ≥ 0.50) but failed (R2 values ≤ 0.25) in 2020. The integration of the VIs with the morphological parameters and the use of PLS-R and SVM models increased the accuracy level for both biophysical parameters (R2 ranged from 0.31 to 0.96) and mitigated the lack of consistency between the years. The SVM model was superior to the PLS-R model in both biophysical parameters. The R2 values for the combined 2019 and 2020 biomass model increased, at the model-testing stage, from 0.62 to 0.96 and the RMSE values dropped from 1778 to 490 kg ha−1. The ability of the SVM model to estimate chickpea biomass and LAI can provide convenient support for different management decisions, including timing and amount of irrigation and harvest date.

Note:
Related Files :
Biomass
Data-fusion
LAI
Machine learning
PLS-R
SVM classifiers
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More details
DOI :
10.1016/j.compag.2022.107581
Article number:
107581
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
63150
Last updated date:
09/01/2023 17:23
Creation date:
09/01/2023 17:23
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Scientific Publication
UAS-based imaging for prediction of chickpea crop biophysical parameters and yield
205

Asaf Avneri
Shlomi Aharon
Anna Brook
Guy Atsmon
Evgeny Smirnov
Roy Sadeh
Shahal Abbo
Zvi Peleg
Ittai Herrmann
David J. Bonfil
Ran Nisim Lati

UAS-based imaging for prediction of chickpea crop biophysical parameters and yield

Chickpea (Cicer arietinum L.) is a key legume crop grown in many semi-arid areas. Traditionally, chickpea is a rainfed spring crop, but in certain countries it has become an irrigated crop. The main objective of this study was to evaluate the ability of Unmanned Aerial Systems (UAS) imaging platform with an integrated RGB camera to provide estimations of leaf area index (LAI), biomass, and yield for chickpea during the irrigation period. Two field trials were conducted in 2019 and 2020, in which chickpea plants were subjected to five and six irrigation regimes, respectively. Eight vegetation indexes (VIs) and three morphological parameters were estimated from the RGB images. In parallel, biomass was determined, LAI was measured manually, and yield was determined at full maturity. In total, 294 plant samples were acquired and analyzed over the two years. Firstly, each of the VIs and morphological parameters were correlated separately against the two biophysical parameters and yield. Then, all the VIs and morphological parameters were analyzed together, and two statistical models, partial least squares regression (PLS-R) and support vector machine (SVM); were used to predict biomass and LAI. The yield was predicted using multi-linear regression (MLR). When each index or morphological parameter was analyzed separately, plant height and some of the VIs provided adequate predictions of the biophysical parameters in 2019 (R2 values ≥ 0.50) but failed (R2 values ≤ 0.25) in 2020. The integration of the VIs with the morphological parameters and the use of PLS-R and SVM models increased the accuracy level for both biophysical parameters (R2 ranged from 0.31 to 0.96) and mitigated the lack of consistency between the years. The SVM model was superior to the PLS-R model in both biophysical parameters. The R2 values for the combined 2019 and 2020 biomass model increased, at the model-testing stage, from 0.62 to 0.96 and the RMSE values dropped from 1778 to 490 kg ha−1. The ability of the SVM model to estimate chickpea biomass and LAI can provide convenient support for different management decisions, including timing and amount of irrigation and harvest date.

Scientific Publication
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