华中农业大学学报2026，Vol.45Issue(3)：115-126,12.DOI:10.13300/j.cnki.hnlkxb.2026.03.010
基于改进YOLO11的番茄叶片病害检测方法
An improved YOLO11-based method of detecting tomato leaf diseases
摘要
Abstract
An improved YOLO11 algorithm integrating multiple modules was proposed to solve the problems of uneven distribution of categories,difficulties in extracting features of small target,and insuffi-cient accuracy in detecting tomato leaf diseases in complex environments.Firstly,the adaptive threshold fo-cal loss(ATFL)was used to replace the loss of traditional cross-entropy.The process of training was opti-mized by implementing a dynamic weight allocation strategy to address the issue of imbalanced samples of diseases,enhance the ability of model to learn features for disease categories with low-frequency and im-prove the overall accuracy of detection.Secondly,the hybrid pooling attention module(HPA)was intro-duced to replace the ordinary convolution in the C3k2 module,and a parallel pooling branch was designed.The ability of extracting spatial feature of small target diseases was strengthened by combining with the group feature recalibration technique to enhance the ability of antiocclusion detection in complex environ-ments.Finally,the normalization in the Conv structure was replaced with re-parameterized batch normaliza-tion(RepBN),and a learnable adjustment parameter was introduced to reconstruct the process of normal-ization to stabilize the feature distribution at the stage of training,accelerate the convergence of model,and enhance the performance of generalization.The results showed that the improved YOLO11 model had excel-lent performance on the dataset of tomato leaf diseases,with increases of 5.8,4.3,2.3,and 1.2 percent-age points in the precision,recall,mAP@0.5,and mAP@0.5:0.95 evaluation metrics compared with that of the YOLO11 base model,respectively.It had good performance on detecting areas of small target diseas-es and scenarios of dense occlusion in particular.It is indicated that the improved YOLO11 algorithm effec-tively improves the performance of the model in detecting tomato leaf diseases,achieving a synchronous im-provement in the accuracy and robustness of detecting tomato leaf diseases.关键词
深度学习/YOLO11/番茄叶片病害检测/损失函数/HPA/RepBNKey words
deep learning/YOLO11/detection of tomato leaf diseases/loss function/hybrid pooling attention module(HPA)/re-parameterized batch normalization(RepBN)分类
农业科技引用本文复制引用
秦皓翔,赵霞,王敏,周慧..基于改进YOLO11的番茄叶片病害检测方法[J].华中农业大学学报,2026,45(3):115-126,12.基金项目
甘肃省自然科学基金项目(24JRRA656) （24JRRA656）
