Wan Liang, Zhu Jiangpeng, Du Xiaoyue, Zhang Jiafei, Han Xiongzhe, Zhou Weijun, Li Xiaopeng, Liu Jianli, Liang Fei, He Yong, Cen Haiyan
College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China.
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China.
J Exp Bot. 2021 Jun 22;72(13):4691-4707. doi: 10.1093/jxb/erab194.
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.
植被覆盖度（FVC）是表征作物育种和精准管理中作物生长状况的关键特征。准确量化不同育种系、品种和生长环境中的FVC具有挑战性，尤其是由于复杂田间条件下存在较大的时空变异性。本研究提出了一种通过将PROSAIL模型与间隙概率模型（PROSAIL-GP）相结合，从无人机（UAV）多光谱图像中对作物FVC进行表型分析的集成建模策略。开展了针对四种主要作物的七次田间试验，并使用配备了RGB和多光谱相机的无人机平台获取冠层图像。PROSAIL-GP模型成功反演了油菜（Brassica napus L.）的FVC，决定系数、均方根误差（RMSE）和相对RMSE（rRMSE）分别为0.79、0.09和18%。在水稻（Oryza sativa L.）、小麦（Triticum aestivum L.）和棉花（Gossypium hirsutum L.）中进一步检验了该方法的稳健性，获得了较高的FVC反演精度，rRMSE分别为12%、6%和6%。我们的研究结果表明，所提出的方法能够在高时空域内从无人机图像中高效反演作物FVC，这应该是精准作物育种的一个有前景的工具。
