Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation
文献类型： 外文期刊
作者： Han, Liang 1 ; Yang, Guijun 2 ; Feng, Haikuan 3 ; Zhou, Chengquan 4 ; Yang, Hao 2 ; Xu, Bo 3 ; Li, Zhenhai 3 ; Yang, Xiao 1 ;
作者机构： 1.Shanxi Datong Univ, Coll Architecture & Geomat Engn, Datong 037003, Peoples R China
2.Beijing Res Ctr Informat Technol Agr, Minist Agr, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
4.Anhui Univ, Sch E
关键词： lodging; nomogram; logistic regression; UAV; maize; features
期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）
ISSN： 2072-4292
年卷期： 2018 年 10 卷 10 期
页码：
收录情况： SCI
摘要： Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.
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