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Article

Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV

College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian District, Beijing 100083, China
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Author to whom correspondence should be addressed.
Plants 2025, 14(11), 1656; https://doi.org/10.3390/plants14111656
Submission received: 9 April 2025 / Revised: 20 May 2025 / Accepted: 28 May 2025 / Published: 29 May 2025
(This article belongs to the Section Crop Physiology and Crop Production)

Abstract

Phyllosticta fragaricola-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.
Keywords: deep learning; strawberry angular leafspot disease; computer vision; severity classification; smart agriculture deep learning; strawberry angular leafspot disease; computer vision; severity classification; smart agriculture

Share and Cite

MDPI and ACS Style

Xu, Y.-X.; Yu, X.-H.; Yi, Q.; Zhang, Q.-Y.; Su, W.-H. Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV. Plants 2025, 14, 1656. https://doi.org/10.3390/plants14111656

AMA Style

Xu Y-X, Yu X-H, Yi Q, Zhang Q-Y, Su W-H. Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV. Plants. 2025; 14(11):1656. https://doi.org/10.3390/plants14111656

Chicago/Turabian Style

Xu, Yi-Xiao, Xin-Hao Yu, Qing Yi, Qi-Yuan Zhang, and Wen-Hao Su. 2025. "Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV" Plants 14, no. 11: 1656. https://doi.org/10.3390/plants14111656

APA Style

Xu, Y.-X., Yu, X.-H., Yi, Q., Zhang, Q.-Y., & Su, W.-H. (2025). Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV. Plants, 14(11), 1656. https://doi.org/10.3390/plants14111656

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