Xu Yi-Xiao, Yu Xin-Hao, Yi Qing, Zhang Qi-Yuan, Su Wen-Hao
College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian District, Beijing 100083, China.
Plants (Basel). 2025 May 29;14(11):1656. doi: 10.3390/plants14111656.
-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.
角斑病在全球草莓生产中造成了巨大的经济损失，因此需要先进的病情严重程度评估方法。本研究提出了一种融合深度学习和计算机视觉的双阶段分级框架。增强版的You Only Look Once version 11（YOLOv11）架构集成了用于改进特征上采样的特征内容感知重组（CARAFE）模块和用于通道级特征重新校准的挤压激励（SE）注意力机制，从而形成了用于草莓角斑病病情严重程度评估的YOLOv11-CARAFE-SE。此外，基于HSV颜色空间中H通道阈值的基于OpenCV的阈值分割算法实现了准确的病斑分割。基于病斑面积与叶片面积的比例，建立了草莓角斑病的病情严重程度分级标准。此外，基于改进的YOLOv11-CARAFE-SE模型和基于OpenCV的算法，开发了用于病情严重程度评估的专用软件。实验结果表明，与基线YOLOv11相比，性能有显著提升：框mAP@0.5提高了1.4%，达到93.2%，掩码mAP@0.5提高了0.9%，达到93.0%，推理时间缩短了0.4毫秒至0.9毫秒，计算量减少了1.94%，降至10.1 GFLOPS。此外，这个两阶段分级框架在检测选定的草莓角斑病样本时平均准确率达到94.2%，为抗性育种计划提供了实时田间诊断和高通量表型分析。这项工作证明了快速估计草莓角斑病严重程度的可行性，这将为田间条件下的草莓病害管理建立一个强大的技术框架。
