A new spectral index for the quantitative identification of yellow rust using fungal spore information

被引:3
|
作者
Ren, Yu [1 ,2 ]
Ye, Huichun [1 ,3 ]
Huang, Wenjiang [1 ,2 ,3 ]
Ma, Huiqin [1 ]
Guo, Anting [1 ,2 ]
Ruan, Chao [1 ,2 ]
Liu, Linyi [1 ]
Qian, Binxiang [1 ,2 ]
机构
[1] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
[2] Univ Chinese Acad Sci, Beijing, Peoples R China
[3] Chinese Acad Sci, Hainan Inst Aerosp Informat, Hainan Key Lab Earth Observat, Sanya, Peoples R China
基金
中国国家自然科学基金;
关键词
Yellow rust; spectral index; fungal spores; quantitative identification; hyperspectral remote sensing; winter wheat; RANDOM FOREST; WATER-STRESS; CLASSIFICATION; ALGORITHMS; DISEASE;
D O I
10.1080/20964471.2021.1907933
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Yellow rust (Puccinia striiformis f. sp. Tritici) is a frequently occurring fungal disease of winter wheat (Triticum aestivum L.). During yellow rust infestation, fungal spores appear on the surface of the leaves as yellow and narrow stripes parallel to the leaf veins. We analyzed the effect of the fungal spores on the spectra of the diseased leaves to find a band sensitive to yellow rust and established a new vegetation index called the yellow rust spore index (YRSI). The estimation accuracy and stability were evaluated using two years of leaf spectral data, and the results were compared with eight indices commonly used for yellow rust detection. The results showed that the use of the YRSI ranked first for estimating the disease ratio for the 2017 spectral data (R-2 = 0.710, RMSE = 0.097) and outperformed the published indices (R-2 = 0.587, RMSE = 0.120) for the validation using the 2002 spectral data. The random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM) algorithms were used to test the discrimination ability of the YRSI and the eight commonly used indices using a mixed dataset of yellow-rust-infested, healthy, and aphid-infested wheat spectral data. The YRSI provided the best performance.
引用
收藏
页码:201 / 216
页数:16
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