文献类型： 外文期刊

作者： Wan, Liang ¹ ; Zhu, Jiangpeng ¹ ; Du, Xiaoyue ¹ ; Zhang, Jiafei ¹ ; Han, Xiongzhe ⁴ ; Zhou, Weijun ⁵ ; Li, Xiaopeng ⁶ ; L ¹ ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

2.Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou 310058, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Spect Sensing, Hangzhou 310058, Peoples R China

4.Kangwon Natl Univ, Coll Agr & Life Sci, Dept Biosyst Engn, Chunchon 24341, Kangwon, South Korea

5.Zhejiang Univ, Coll Agr & Biotechnol, Hangzhou 310058, Peoples R China

6.Chinese Acad Sci, Inst Soil Sci, Nanjing 210008, Peoples R China

7.Xinjiang Acad Agr & Reclamat Sci, Inst Farmland Water Conservancy & Soil Fertilizer, Shihezi 832000, Peoples R China

关键词： Canopy coverage; drone; leaf angle distribution; leaf area index; multispectral images; PROSAIL-GP model; unmanned aerial vehicle

期刊名称：JOURNAL OF EXPERIMENTAL BOTANY （ 影响因子：6.992； 五年影响因子：7.86 ）

ISSN： 0022-0957

年卷期： 2021 年 72 卷 13 期

页码：

收录情况： SCI

摘要： 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 multi-spectral 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.