Research on Detection of Major Diseases in Mung Bean Leaves Based on an Improved YOLOv8

doi:10.13560/j.cnki.biotech.bull.1985.2025-1295

Abstract

Abstract:

Objective To address issues such as complex detection scenarios, insufficient detection accuracy, and weak small-target detection capability for mung bean leaf diseases in natural environments, this study proposes a deep learning algorithm named YOLOv8-EMA-CARAFE-Layer. Method The EMA (efficient multi-scale attention) mechanism was introduced into the backbone network to enhance the model’s ability to extract and screen disease features. The CARAFE (Content-Aware ReAssembly of FEatures) upsampling module was adopted in the neck network, which enhanced global information aggregation capability by dynamically generating upsampling kernels, thereby improving feature reconstruction accuracy. A small-target detection layer was added to the neck and head networks to improve the detection effectiveness for minute disease targets. Result The improved model achieved a mAP@50 of 91.0%, a precision of 86.9%, and a recall of 83.4%. Compared with mainstream object detection models YOLOv3, YOLOv5, YOLOv6, and YOLOv11, the mAP@50 improved by 6.8%, 0.3%, 13.5%, and 1.6% respectively, demonstrating superior performance in mung bean leaf disease detection. Furthermore, through lightweight design, the model maintained a low parameter count of only 5.95 MB. Conclusion YOLOv8-EMA-CARAFE-Layer model enables deployment on mobile terminals such as drones and smart agricultural machinery, offering practical application value for enhancing the rapid, efficient, and accurate detection of major diseases in mung bean leaves.

Key words: image recognition, deep learning, YOLOv8, mung bean leaf diseases, efficient multi-scale attention mechanism

WANG Chao, YANG Yu-chao, GUO Si-yu, JIA Yao-xuan, CHEN Yan-xia, ZHAO Yu, QIAO Xing-xing, XIAO Lu-jie, FENG Mei-chen, YANG Wu-de. Research on Detection of Major Diseases in Mung Bean Leaves Based on an Improved YOLOv8[J]. Biotechnology Bulletin, 2026, 42(10): 282-292.

Figures/Tables 13

References 30

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病害名称 Disease types	病害特征 Disease characters
细菌性疫病 Bacterial blight	初期叶片表面出现小的水浸斑，变成淡黄色到棕褐色；后期病斑在叶片上扩展，病斑呈黑色
轮纹病 Ring spot	初期叶片产生小的、褐色圆形病斑；后期发展成大的具有同心环的圆形或不规则的深褐斑
叶斑病 Leaf spot	初期叶片上出现小型不规则或圆形褐色斑点，边缘清晰；后期病斑扩展，形成大的不规则坏死区域
健康 Health	叶片呈深绿色，叶形卵状，叶脉清晰可见，质地柔软薄嫩，表面光滑

病害名称 Disease types	病害特征 Disease characters
细菌性疫病 Bacterial blight	初期叶片表面出现小的水浸斑，变成淡黄色到棕褐色；后期病斑在叶片上扩展，病斑呈黑色
轮纹病 Ring spot	初期叶片产生小的、褐色圆形病斑；后期发展成大的具有同心环的圆形或不规则的深褐斑
叶斑病 Leaf spot	初期叶片上出现小型不规则或圆形褐色斑点，边缘清晰；后期病斑扩展，形成大的不规则坏死区域
健康 Health	叶片呈深绿色，叶形卵状，叶脉清晰可见，质地柔软薄嫩，表面光滑

模型	精确度 Precision（%）	召回率 Recall（%）	mAP@50（%）	mAP@50-95（%）	参数量 Params（MB）
YOLOv8n	85.5	81.1	86.2	62.8	5.73
YOLOv8-EMA-CARAFE-Layer	86.9	83.4	91.0	67.5	5.95

模型	精确度 Precision（%）	召回率 Recall（%）	mAP@50（%）	mAP@50-95（%）	参数量 Params（MB）
YOLOv8n	85.5	81.1	86.2	62.8	5.73
YOLOv8-EMA-CARAFE-Layer	86.9	83.4	91.0	67.5	5.95

模型 Model	精确度 Precision（%）	召回率 Recall（%）	mAP@50（%）	mAP@50-95（%）	参数量 Params （MB）
YOLOv8_CBAM	84.5	78.6	86.8	64.3	5.86
YOLOv8_CA	87.4	80.3	86.7	64.0	5.75
YOLOv8_EMA	83.1	81.2	87.4	64.0	5.73