A new spectral index for the quantitative identification of yellow rust using fungal spore information. Issue 2 (3rd April 2021)

• Record Type: Journal Article
• Authors: Ren, Yu; Ye, Huichun; Huang, Wenjiang; Ma, Huiqin; Guo, Anting; Ruan, Chao; Liu, Linyi; Qian, Binxiang
• Is Part Of: Big earth data. Volume 5:Issue 2(2021)
• Page Start: 201
• Page End: 216
• Publication Date: 2021-04-03
• Subjects: Yellow rust -- spectral index -- fungal spores -- quantitative identification -- hyperspectral remote sensing -- winter wheat; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2021.1907933
• Languages: English
• ISSNs: 2096-4471

Abstract

ABSTRACT: 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.