现代农业装备2025，Vol.46Issue(2)：86-92,7.DOI:10.3969/j.issn.1673-2154.2025.02.0013
融合多尺度注意力机制的棉花枯萎病识别算法研究
Research on Cotton Blight Recognition Algorithm Based on Multi-scale Attention Mechanism
摘要
Abstract
Aiming at the problems such as complex leaf background,occlusion and multi-scale lesions in cotton blight detection,a novel YOLOv7 cotton blight recognition algorithm combining multi-scale attention mechanism was proposed.In order to adapt to the different scales and shapes of cotton blight spots and improve the recognition and detection effect,a multi-scale attention module was first added to the feature extraction network of YOLOv7,and the generalization performance of the model was improved through multi-scale information fusion and adaptive weight adjustment mechanism.At the same time,in order to reduce the calculation amount and parameter number of the model and improve the running speed of the model,the feature extraction network was changed to InceptionNeXt.Experimental results showed that the detection accuracy of the improved YOLOv7 model was up to 95.9％,2.3％higher than that of the baseline model,and the average accuracy mAP@0.5 was up to 88.15％,3.67％higher.The recall rate R reached 94.65％,got an increase of 2.31％.The number of parameters decreased by 2.78 M to 33.73 M,and the calculation amount decreased by 14.62 G to 89.65 G.The results showed that the improved algorithm could effectively improve the accuracy and efficiency of cotton blight identification,and provided some technical support for disease control.关键词
YOLOv7/棉花/注意力机制/枯萎病/病害检测Key words
YOLOv7/cotton/attention mechanism/blight/disease detection分类
信息技术与安全科学引用本文复制引用
李文雪,孟洪兵,孙丽丽,韩璐宇..融合多尺度注意力机制的棉花枯萎病识别算法研究[J].现代农业装备,2025,46(2):86-92,7.基金项目
新疆生产建设兵团财政科技计划项目(1121DB008) （1121DB008）
