Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV
<i>Phyllosticta fragaricola</i>-induced angular leaf spot causes substantial economic losses in global strawberry production, necessitating advanced severity assessment methods. This study proposed a dual-phase grading framework integrating deep learning and computer vision. The enhanced...
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MDPI AG
2025-05-01
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| author | Yi-Xiao Xu Xin-Hao Yu Qing Yi Qi-Yuan Zhang Wen-Hao Su |
| author_facet | Yi-Xiao Xu Xin-Hao Yu Qing Yi Qi-Yuan Zhang Wen-Hao Su |
| author_sort | Yi-Xiao Xu |
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| description | <i>Phyllosticta fragaricola</i>-induced angular leaf spot causes substantial economic losses in global strawberry production, necessitating advanced severity assessment methods. This study proposed a dual-phase grading framework integrating deep learning and computer vision. The enhanced You Only Look Once version 11 (YOLOv11) architecture incorporated a Content-Aware ReAssembly of FEatures (CARAFE) module for improved feature upsampling and a squeeze-and-excitation (SE) attention mechanism for channel-wise feature recalibration, resulting in the YOLOv11-CARAFE-SE for the severity assessment of strawberry angular leaf spot. Furthermore, an OpenCV-based threshold segmentation algorithm based on H-channel thresholds in the HSV color space achieved accurate lesion segmentation. A disease severity grading standard for strawberry angular leaf spot was established based on the ratio of lesion area to leaf area. In addition, specialized software for the assessment of disease severity was developed based on the improved YOLOv11-CARAFE-SE model and OpenCV-based algorithms. Experimental results show that compared with the baseline YOLOv11, the performance is significantly improved: the box mAP@0.5 is increased by 1.4% to 93.2%, the mask mAP@0.5 is increased by 0.9% to 93.0%, the inference time is shortened by 0.4 ms to 0.9 ms, and the computational load is reduced by 1.94% to 10.1 GFLOPS. In addition, this two-stage grading framework achieves an average accuracy of 94.2% in detecting selected strawberry horn leaf spot disease samples, providing real-time field diagnostics and a high-throughput phenotypic analysis for resistance breeding programs. This work demonstrates the feasibility of rapidly estimating the severity of strawberry horn leaf spot, which will establish a robust technical framework for strawberry disease management under field conditions. |
| format | Article |
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| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Plants |
| spelling | doaj-art-d18b84762e084f1da3f392f45cc84b6a2025-08-20T03:11:19ZengMDPI AGPlants2223-77472025-05-011411165610.3390/plants14111656Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCVYi-Xiao Xu0Xin-Hao Yu1Qing Yi2Qi-Yuan Zhang3Wen-Hao Su4College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian District, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian District, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian District, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian District, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian District, Beijing 100083, China<i>Phyllosticta fragaricola</i>-induced angular leaf spot causes substantial economic losses in global strawberry production, necessitating advanced severity assessment methods. This study proposed a dual-phase grading framework integrating deep learning and computer vision. The enhanced You Only Look Once version 11 (YOLOv11) architecture incorporated a Content-Aware ReAssembly of FEatures (CARAFE) module for improved feature upsampling and a squeeze-and-excitation (SE) attention mechanism for channel-wise feature recalibration, resulting in the YOLOv11-CARAFE-SE for the severity assessment of strawberry angular leaf spot. Furthermore, an OpenCV-based threshold segmentation algorithm based on H-channel thresholds in the HSV color space achieved accurate lesion segmentation. A disease severity grading standard for strawberry angular leaf spot was established based on the ratio of lesion area to leaf area. In addition, specialized software for the assessment of disease severity was developed based on the improved YOLOv11-CARAFE-SE model and OpenCV-based algorithms. Experimental results show that compared with the baseline YOLOv11, the performance is significantly improved: the box mAP@0.5 is increased by 1.4% to 93.2%, the mask mAP@0.5 is increased by 0.9% to 93.0%, the inference time is shortened by 0.4 ms to 0.9 ms, and the computational load is reduced by 1.94% to 10.1 GFLOPS. In addition, this two-stage grading framework achieves an average accuracy of 94.2% in detecting selected strawberry horn leaf spot disease samples, providing real-time field diagnostics and a high-throughput phenotypic analysis for resistance breeding programs. This work demonstrates the feasibility of rapidly estimating the severity of strawberry horn leaf spot, which will establish a robust technical framework for strawberry disease management under field conditions.https://www.mdpi.com/2223-7747/14/11/1656deep learningstrawberry angular leafspot diseasecomputer visionseverity classificationsmart agriculture |
| spellingShingle | Yi-Xiao Xu Xin-Hao Yu Qing Yi Qi-Yuan Zhang Wen-Hao Su Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV Plants deep learning strawberry angular leafspot disease computer vision severity classification smart agriculture |
| title | Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV |
| title_full | Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV |
| title_fullStr | Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV |
| title_full_unstemmed | Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV |
| title_short | Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV |
| title_sort | dual phase severity grading of strawberry angular leaf spot based on improved yolov11 and opencv |
| topic | deep learning strawberry angular leafspot disease computer vision severity classification smart agriculture |
| url | https://www.mdpi.com/2223-7747/14/11/1656 |
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