Quantifying the severity of Marssonina blotch on apple leaves: development and validation of a novel spectral index

文献类型： 外文期刊

第一作者： Zhang, Wenjie

作者： Zhang, Wenjie;Zhang, Chengjian;Chen, Riqiang;Wu, Baoguo;Zhao, Chunjiang;Zhang, Wenjie;Zhang, Chengjian;Chen, Riqiang;Xu, Bo;Yang, Hao;Feng, Haikuan;Zhao, Dan;Zhao, Chunjiang;Yang, Guijun;Yang, Guijun

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关键词： Apple; Disease detection; Hyperspectral imaging; Disease severity; Noninvasive

期刊名称：PLANT METHODS （ 影响因子：4.4； 五年影响因子：5.7 ）

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年卷期： 2025 年 21 卷 1 期

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收录情况： SCI

摘要： Apple Marssonina blotch (AMB) is a major disease causing pre-mature defoliation. The occurrence of AMB will lead to serious production decline and economic losses. The precise identification of AMB outbreaks and the measurement of its severity are essential for limiting the spread of the disease, yet this issue remains unaddressed to this day. Given these, we conducted experiments in Qian County, Shaanxi, China, to develop an Apple Marssonina Blotch Index (AMBI) based on hyperspectral imaging, aimed to quantify disease severity at the leaf scale and to monitor infection at the canopy scale. Based on the separability and combination of individual band, characteristic wavelengths were identified in green band, red edge band and near-infrared band to construct AMBI = (R762nm-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document} R534nm)/(R534nm+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{document} R690nm). The results demonstrated that AMBI exhibited high overall accuracies (R2 = 0.89, RMSE = 9.67%) in estimating the disease ratio at the leaf scale compared to commonly used indices. At the canopy scale, AMBI enabled effective classification of healthy and diseased trees, yielding an overall accuracy (OA) of 89.09% and a Kappa coefficient of 0.78. Furthermore, analysis of unmanned aerial vehicle (UAV) acquired hyperspectral imagery using AMBI enabled the spatial mapping of diseased tree distribution, highlighting its potential as a scalable and timely tool for precision orchard disease surveillance.

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