干旱区生态环境知识资源中心(Arid): UAS-based imaging for prediction of chickpea crop biophysical parameters and yield

Arid

DOI	10.1016/j.compag.2022.107581
	UAS-based imaging for prediction of chickpea crop biophysical parameters and yield
	Avneri, Asaf; Aharon, Shlomi; Brook, Anna; Atsmon, Guy; Smirnov, Evgeny; Sadeh, Roy; Abbo, Shahal; Peleg, Zvi; Herrmann, Ittai; Bonfil, David J.; Lati, Ran Nisim
通讯作者	Lati, RN
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2023
卷号	205
英文摘要	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.
英文关键词	Biomass Data -fusion LAI Machine learning PLS-R SVM
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000913350800001
WOS关键词	VEGETATION INDEXES ; GRAIN-YIELD ; BRASSICA-NAPUS ; DATA FUSION ; SEED YIELD ; BIOMASS ; WHEAT ; SURFACE ; HEIGHT ; REGRESSION
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395814
推荐引用方式 GB/T 7714	Avneri, Asaf,Aharon, Shlomi,Brook, Anna,et al. UAS-based imaging for prediction of chickpea crop biophysical parameters and yield[J],2023,205.
APA	Avneri, Asaf.,Aharon, Shlomi.,Brook, Anna.,Atsmon, Guy.,Smirnov, Evgeny.,...&Lati, Ran Nisim.(2023).UAS-based imaging for prediction of chickpea crop biophysical parameters and yield.COMPUTERS AND ELECTRONICS IN AGRICULTURE,205.
MLA	Avneri, Asaf,et al."UAS-based imaging for prediction of chickpea crop biophysical parameters and yield".COMPUTERS AND ELECTRONICS IN AGRICULTURE 205(2023).