A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles. (8th May 2021)

• Record Type: Journal Article
• Authors: Wan, Liang; Zhu, Jiangpeng; Du, Xiaoyue; Zhang, Jiafei; Han, Xiongzhe; Zhou, Weijun; Li, Xiaopeng; Liu, Jianli; Liang, Fei; He, Yong; Cen, Haiyan
• Editors: Rogers, Alistair
• Is Part Of: Journal of experimental botany. Volume 72:Number 13(2021)
• Page Start: 4691
• Page End: 4707
• Publication Date: 2021-05-08
• Subjects: Canopy coverage -- drone -- leaf angle distribution -- leaf area index -- multispectral images -- PROSAIL-GP model -- unmanned aerial vehicle; Botany -- Periodicals; Botany, Experimental -- Periodicals; Plant physiology -- Periodicals; 580
• DOI: 10.1093/jxb/erab194
• Languages: English
• ISSNs: 0022-0957

Abstract

Abstract : We developed a mechanistic model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles, which is applicable to oilseed rape, rice, wheat, and cotton, with high accuracy. Abstract: Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape ( Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice ( Oryza sativa L.), wheat ( Triticum aestivum L.), and cotton ( Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.