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<p>Apple <i>Marssonina</i> 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 <i>Marssonina</i> 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 = (R<sub>762nm</sub> <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13007_2025_1414_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(-\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation> R<sub>534nm</sub>)/(R<sub>534nm</sub> <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13007_2025_1414_Article_IEq2.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">\(+\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>+</mo> </math></EquationSource> </InlineEquation> R<sub>690nm</sub>). The results demonstrated that AMBI exhibited high overall accuracies (R<sup>2</sup> = 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月25日

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

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 \(-\) - R534nm)/(R534nm \(+\) + 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.