中国农机化学报2026，Vol.47Issue(1)：52-61,10.DOI:10.13733/j.jcam.issn.2095-5553.2026.01.008
基于ALD—YOLO的自然场景下苹果叶片病害检测方法研究
Research on apple leaf disease detection in natural scenes based on ALD—YOLO
摘要
Abstract
To address the challenges in apple leaf disease detection in natural scenes,such as background noise,similar disease symptoms,and a high density of small-scale targets,this study proposes the ALD—YOLO algorithm to achieve accurate detection of apple leaf diseases in natural environments.First,a CBAM—G attention mechanism is proposed and integrated into the backbone network,enabling the network to focus on key disease features during feature extraction while reducing the impact of image distortions.Second,multi-level feature information is fed into an OCR module and then returned to the head network,providing the model with richer pixel-level semantic information for precise classification.Finally,a dynamic weighted IoU loss function is proposed,dynamically assigning weights to small-scale targets to increase their loss,thereby improving detection accuracy for such targets.Experimental results demonstrate that the algorithm achieves an accuracy of 84.93%,a recall rate of 72.88%,and a mean average precision(mAP)of 77.48%on a custom dataset,with a processing speed of 24.74 frames per second.关键词
苹果/叶片病害检测/YOLOX/自然场景/OCR模块Key words
apple/leaf disease detection/YOLOX/natural scenes/OCR module分类
农业科技引用本文复制引用
Liu Xia,Zhou Jiaheng,Gu Qinghui..基于ALD—YOLO的自然场景下苹果叶片病害检测方法研究[J].中国农机化学报,2026,47(1):52-61,10.基金项目
国家自然科学基金(31801258) （31801258）
2023年度湖北省教育厅科学研究计划指导性项目(B2023612) （B2023612）
