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Classification of Southern Corn Rust Severity Based on Leaf-Level Hyperspectral Data Collected under Solar Illumination

文献类型： 外文期刊

作者： Gao, Jianmeng 1 ; Ding, Mingliang 1 ; Sun, Qiuyu 1 ; Dong, Jiayu 1 ; Wang, Huanyi 3 ; Ma, Zhanhong 1 ;

作者机构： 1.China Agr Univ, Coll Plant Protect, Dept Plant Pathol, Beijing 100193, Peoples R China

2.Yunnan Acad Agr Sci, Food Crop Res Inst, Kunming 650205, Yunnan, Peoples R China

3.China Agr Univ, Coll Plant Protect, Dept Entomol, Beijing 100193, Peoples R China

关键词： southern corn rust; disease; hyperspectral; ANOVA; random forest; classification

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

ISSN：

年卷期： 2022 年 14 卷 11 期

页码：

收录情况： SCI

摘要： Maize is one of the most important crops in China, and it is under a serious, ever-increasing threat from southern corn rust (SCR). The identification of wheat rust based on hyperspectral data has been proved effective, but little research on detecting maize rust has been reported. In this study, full-range hyperspectral data (350 similar to 2500 nm) were collected under solar illumination, and spectra collected under solar illumination (SCUSI) were separated into several groups according to the disease severity, measuring height and leaf curvature (the smoothness of the leaf surface). Ten indices were selected as candidate indicators for SCR classification, and their sensitivities to the disease severity, measuring height and leaf curvature, were subjected to analysis of variance (ANOVA). The better-performing indices according to the ANOVA test were applied to a random forest classifier, and the classification results were evaluated by using a confusion matrix. The results indicate that the PRI was the optimal index for SCR classification based on the SCUSI, with an overall accuracy of 81.30% for mixed samples. The results lay the foundation for SCR detection in the incubation period and reveal potential for SCR detection based on UAV and satellite imageries, which may provide a rapid, timely and cost-effective detection method for SCR monitoring.

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