农业机械学报2026，Vol.57Issue(18)：70-80,105,12.DOI:10.6041/j.issn.1000-1298.2026.18.007
基于改进YOLO 11s的轻量化大豆炭疽病孢子检测方法
Lightweight Soybean Anthracnose Spore Detection Method Based on Improved YOLO 11s
摘要
Abstract
Soybean anthracnose is caused by the pathogen Colletotrichum truncatum,and its conidia,the soybean anthracnose spores can spread rapidly with rain,seriously threatening soybean yield and quality.Spore detection is a critical foundation for early disease monitoring and precise control.To address issues such as the small target scale of soybean anthracnose spores,large number of parameters in existing detection models,high computational complexity,and difficulty of meeting edge device deployment requirements,a lightweight soybean anthracnose spore detection model,HAD-YOLO,was proposed based on the improved YOLO 11s.Firstly,the backbone network introduced the ADown downsampling module,using multi-path pooling and channel separation mechanisms to enhance edge and detail feature extraction of spores while reducing model complexity.Secondly,the neck network adopted the high-level screening feature pyramid network(HS-FPN)instead of the original PANet structure,strengthening multi-scale feature representation through channel compression and dynamic screening fusion mechanisms and improving the encoding and localisation capability for small spore targets.Additionally,the detection head was designed as a lightweight shared convolutional detection head(LSCD),further reducing model parameters and computational load through grouped convolution and weight-sharing strategies while maintaining spore localisation and classification performance under low-magnification microscopic scenarios.Finally,the bounding box regression loss function used WIOU v3 instead of complete intersection over union(CIoU),mitigating low-quality sample gradient interference through a dynamic focusing mechanism,enhancing model training stability and generalisation ability.Experimental results on a self-built dataset showed that the HAD-YOLO model achieved precision,recall,and mAP50 of 92.3％,85.3％,and 90.9％,respectively,improving over the baseline model by 3.4,3.0,and 4.1 percentage points,while reducing model parameters,computational complexity,and weight file size by 46.8％,36.6％,and 46.4％,respectively.Deployment tests on the Raspberry Pi 4B platform showed an average inference time of 57.5 ms per image,demonstrating favourable real-time detection capability.The research result indicated that this method achieved model lightweighting while ensuring detection precision,providing a methodological foundation for intelligent microscopic image detection and edge deployment of soybean anthracnose spores.关键词
大豆炭疽病/孢子检测/轻量化/改进YOLO 11s/ADownKey words
soybean anthracnose/spore detection/lightweight/improved YOLO 11s/ADown分类
信息技术与安全科学引用本文复制引用
雷雨,何梦奇,阮瑞,阮超,杨雪,赵晋陵,黄林生..基于改进YOLO 11s的轻量化大豆炭疽病孢子检测方法[J].农业机械学报,2026,57(18):70-80,105,12.基金项目
国家自然科学基金项目(32301701)、安徽省高等学校科学研究项目(2022AH050085)、河南省重点研发专项(241111110800)、安徽省自然科学基金项目(2508085QD124)和合肥市自然科学基金项目(202309) （32301701）
