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标题：A new spectral index for the quantitative identification of yellow rust using fungal spore information
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作者：Yu Ren ; Huichun Ye ; Wenjiang Huang 等
期刊名称：Big Earth Data
印刷版ISSN：2096-4471
电子版ISSN：2574-5417
出版年度：2021
卷号：5
期号：2
页码：201-216
DOI：10.1080/20964471.2021.1907933
语种：English
出版社：Taylor & Francis Group
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 (R2 = 0.710, RMSE = 0.097) and outperformed the published indices (R2 = 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.
关键词：Yellow rust;spectral index;fungal spores;quantitative identification;hyperspectral remote sensing;winter wheat