Quantifying the severity of Marssonina blotch on apple leaves: development and validation of a novel spectral index
Abstract 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 di...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-025-01414-4 |
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| author | Wenjie Zhang Chengjian Zhang Riqiang Chen Bo Xu Hao Yang Haikuan Feng Dan Zhao Baoguo Wu Chunjiang Zhao Guijun Yang |
| author_facet | Wenjie Zhang Chengjian Zhang Riqiang Chen Bo Xu Hao Yang Haikuan Feng Dan Zhao Baoguo Wu Chunjiang Zhao Guijun Yang |
| author_sort | Wenjie Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-ed7e2264cc3f41f1ac7b025c95e98805 |
| institution | Kabale University |
| issn | 1746-4811 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-ed7e2264cc3f41f1ac7b025c95e988052025-08-20T03:42:53ZengBMCPlant Methods1746-48112025-07-0121111510.1186/s13007-025-01414-4Quantifying the severity of Marssonina blotch on apple leaves: development and validation of a novel spectral indexWenjie Zhang0Chengjian Zhang1Riqiang Chen2Bo Xu3Hao Yang4Haikuan Feng5Dan Zhao6Baoguo Wu7Chunjiang Zhao8Guijun Yang9School of Information Science & Technology, Beijing Forestry UniversitySchool of Information Science & Technology, Beijing Forestry UniversitySchool of Information Science & Technology, Beijing Forestry UniversityKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry SciencesKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry SciencesKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry SciencesKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry SciencesSchool of Information Science & Technology, Beijing Forestry UniversitySchool of Information Science & Technology, Beijing Forestry UniversityKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry SciencesAbstract 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.https://doi.org/10.1186/s13007-025-01414-4AppleDisease detectionHyperspectral imagingDisease severityNoninvasive |
| spellingShingle | Wenjie Zhang Chengjian Zhang Riqiang Chen Bo Xu Hao Yang Haikuan Feng Dan Zhao Baoguo Wu Chunjiang Zhao Guijun Yang Quantifying the severity of Marssonina blotch on apple leaves: development and validation of a novel spectral index Plant Methods Apple Disease detection Hyperspectral imaging Disease severity Noninvasive |
| title | Quantifying the severity of Marssonina blotch on apple leaves: development and validation of a novel spectral index |
| title_full | Quantifying the severity of Marssonina blotch on apple leaves: development and validation of a novel spectral index |
| title_fullStr | Quantifying the severity of Marssonina blotch on apple leaves: development and validation of a novel spectral index |
| title_full_unstemmed | Quantifying the severity of Marssonina blotch on apple leaves: development and validation of a novel spectral index |
| title_short | Quantifying the severity of Marssonina blotch on apple leaves: development and validation of a novel spectral index |
| title_sort | quantifying the severity of marssonina blotch on apple leaves development and validation of a novel spectral index |
| topic | Apple Disease detection Hyperspectral imaging Disease severity Noninvasive |
| url | https://doi.org/10.1186/s13007-025-01414-4 |
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