农业工程2025，Vol.15Issue(4)：21-27,7.DOI:10.19998/j.cnki.2095-1795.202504304
基于改进YOLOv7的荔枝叶片病害监测模型
Litchi leaf disease monitoring model based on improved YOLOv7
摘要
Abstract
To accurately and promptly detect litchi anthracnose among complex natural environmental conditions,a litchi disease recog-nition method with improved YOLOv7 was proposed.SPPCSPC structure was reconstructed,convolutional layers were pruned,and pooling structure was modified to reduce module complexity and accelerate network convergence speed.To allocate resources reasonably,GAM attention mechanism was introduced.To improve detection accuracy,WIoU loss function was employed.Experimental results indicated that improved YOLOv7 took 0.18 s to detect a single image,with a memory usage of 41.45 MB,and an average accuracy mean of 80.27%.Compared to YOLOv7,memory usage was reduced by 34.5%,detection speed was increased by 60%,and model performance outperformed other models such as Faster R-CNN and YOLOv5.This method provided accurate and rapid detection of lit-chi disease targets in complex natural environments and unstructured backgrounds,and provided a reference for real-time monitoring re-search of economic fruit tree leaf diseases.关键词
荔枝/病害监测/YOLOv7/注意力机制Key words
litchi/disease monitoring/YOLOv7/attention mechanism分类
农业科技引用本文复制引用
周平,殷勇,杨岚,钱康亮..基于改进YOLOv7的荔枝叶片病害监测模型[J].农业工程,2025,15(4):21-27,7.基金项目
泸州市科技计划资助重点研发项目(2021-NYF-17) （2021-NYF-17）
