Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning models
Early monitoring of cotton Verticillium wilt (VW) is crucial for preventing significant yield losses and quality deterioration. Current hyperspectral approaches often overlook the bottom-up disease progression and the impact of leaf stratification on VW detection. To address this, vertical spectral...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1599877/full |
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| author | Yi Gao Yi Gao Changping Huang Changping Huang Xia Zhang Ze Zhang Bing Chen |
| author_facet | Yi Gao Yi Gao Changping Huang Changping Huang Xia Zhang Ze Zhang Bing Chen |
| author_sort | Yi Gao |
| collection | DOAJ |
| description | Early monitoring of cotton Verticillium wilt (VW) is crucial for preventing significant yield losses and quality deterioration. Current hyperspectral approaches often overlook the bottom-up disease progression and the impact of leaf stratification on VW detection. To address this, vertical spectral traits were examined to improve early diagnosis. A total of 551 in-situ leaf spectra were averaged from thousands of measurements, alongside corresponding RGB images from top, middle, and bottom leaf layers. Five severity levels (SL=0-4) were classified based on lesion coverage. Various vegetation indices and signal features were extracted for VW identification. Three feature selection methods, Relief-F, Lasso, and Random Forest (RF), were integrated with five machine learning models, including LightGBM, ANN, XGBoost, RF, and SVM. Results showed that spectral reflectance varied significantly by severity and layer, with the most pronounced variations in the bottom layer’s visible spectrum. LightGBM with RF-selected features achieved the best performance and fastest training, with accuracies of 0.82, 0.81, and 0.91 for the top, middle, and bottom leaf layers, respectively. Early-stage detection (SL=0-2) was most effective in the lowest layer, showing 38% and 34% higher precision (SL=1) than the upper two. Critical spectral features varied with vertical leaf layers and disease severity, with blue and red-edge bands identified as most important. For assessing five disease severity levels, the most informative features for the top, middle, and bottom layers were AntGitelson, Blue Index (B), and PRI570. For detecting early symptoms (SL=1), the blue band was particularly effective, followed by water-related bands. At the initial infection stage, the most significant indicators for top, middle, and bottom layers were Blue/red index (BRI), B, and WSCT, respectively. This study deepens understanding of vertical leaf spectral dynamics and enables rapid, non-destructive in vivo detection of cotton Verticillium wilt, enhancing the applicability of portable hyperspectral devices and informing leaf-layer-aware precision disease management strategies. |
| format | Article |
| id | doaj-art-839b1a6deee74868a1ecbf62c8712c4d |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-839b1a6deee74868a1ecbf62c8712c4d2025-08-20T02:10:28ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-06-011610.3389/fpls.2025.15998771599877Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning modelsYi Gao0Yi Gao1Changping Huang2Changping Huang3Xia Zhang4Ze Zhang5Bing Chen6National Engineering Research Center of Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaNational Engineering Research Center of Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaNational Engineering Research Center of Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaXinjiang Production and Construction Corps Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, Xinjiang, ChinaResearch Institute, Xinjiang Academy Agricultural and Reclamation Science, Shihezi, ChinaEarly monitoring of cotton Verticillium wilt (VW) is crucial for preventing significant yield losses and quality deterioration. Current hyperspectral approaches often overlook the bottom-up disease progression and the impact of leaf stratification on VW detection. To address this, vertical spectral traits were examined to improve early diagnosis. A total of 551 in-situ leaf spectra were averaged from thousands of measurements, alongside corresponding RGB images from top, middle, and bottom leaf layers. Five severity levels (SL=0-4) were classified based on lesion coverage. Various vegetation indices and signal features were extracted for VW identification. Three feature selection methods, Relief-F, Lasso, and Random Forest (RF), were integrated with five machine learning models, including LightGBM, ANN, XGBoost, RF, and SVM. Results showed that spectral reflectance varied significantly by severity and layer, with the most pronounced variations in the bottom layer’s visible spectrum. LightGBM with RF-selected features achieved the best performance and fastest training, with accuracies of 0.82, 0.81, and 0.91 for the top, middle, and bottom leaf layers, respectively. Early-stage detection (SL=0-2) was most effective in the lowest layer, showing 38% and 34% higher precision (SL=1) than the upper two. Critical spectral features varied with vertical leaf layers and disease severity, with blue and red-edge bands identified as most important. For assessing five disease severity levels, the most informative features for the top, middle, and bottom layers were AntGitelson, Blue Index (B), and PRI570. For detecting early symptoms (SL=1), the blue band was particularly effective, followed by water-related bands. At the initial infection stage, the most significant indicators for top, middle, and bottom layers were Blue/red index (BRI), B, and WSCT, respectively. This study deepens understanding of vertical leaf spectral dynamics and enables rapid, non-destructive in vivo detection of cotton Verticillium wilt, enhancing the applicability of portable hyperspectral devices and informing leaf-layer-aware precision disease management strategies.https://www.frontiersin.org/articles/10.3389/fpls.2025.1599877/fullcotton Verticillium wiltvertical leaf layerhyperspectral reflectancemachine learningdisease severity |
| spellingShingle | Yi Gao Yi Gao Changping Huang Changping Huang Xia Zhang Ze Zhang Bing Chen Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning models Frontiers in Plant Science cotton Verticillium wilt vertical leaf layer hyperspectral reflectance machine learning disease severity |
| title | Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning models |
| title_full | Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning models |
| title_fullStr | Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning models |
| title_full_unstemmed | Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning models |
| title_short | Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning models |
| title_sort | vertical stratification enabled early monitoring of cotton verticillium wilt using in situ leaf spectroscopy via machine learning models |
| topic | cotton Verticillium wilt vertical leaf layer hyperspectral reflectance machine learning disease severity |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1599877/full |
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