[1]吴建军,赵惠圆,祝玉华.葡萄叶片病害智能检测技术研究进展[J].江苏农业学报,2026,42(04):842-854.[doi:doi:10.3969/j.issn.1000-4440.2026.04.021]
　WU Jianjun,ZHAO Huiyuan,ZHU Yuhua.Research progress on intelligent detection technology for grape leaf diseases[J].,2026,42(04):842-854.[doi:doi:10.3969/j.issn.1000-4440.2026.04.021]
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葡萄叶片病害智能检测技术研究进展()

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
42
期数:
2026年04期
页码:
842-854
栏目:
综述
出版日期:
2026-04-30

文章信息/Info

Title:
Research progress on intelligent detection technology for grape leaf diseases
作者:
吴建军1; 2; 3; 赵惠圆1; 2; 祝玉华1; 2
(1.河南工业大学信息科学与工程学院,河南郑州450001；2.河南工业大学粮食信息处理与控制教育部重点实验室,河南郑州450001；3.国家粮食和物资储备局科学研究院,北京100037)
Author(s):
WU Jianjun1; 2; 3; ZHAO Huiyuan1; 2; ZHU Yuhua1; 2
（1.College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China;2.Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China;3.Academy of National Food and Strategic Reserves Administration, Beijing 100037, China）
关键词:
葡萄; 病害; 智能检测; 深度学习; 卷积神经网络; YOLO算法
Keywords:
grape; disease; intelligent detection; deep learning; convolutional neural network; YOLO algorithm
分类号:
S663.1
DOI:
doi:10.3969/j.issn.1000-4440.2026.04.021
文献标志码:
A
摘要:
葡萄叶片病害严重威胁葡萄产业可持续发展,传统人工诊断存在主观性强、效率低、时效性差等问题,难以满足现代农业发展需求。葡萄叶片病害智能检测早期主要基于支持向量机、K近邻、随机森林等传统机器学习算法,识别准确率从60%逐步提升至85%～90%。卷积神经网络通过Inception模块、残差连接、密集连接等架构创新,有效解决了多尺度特征提取难题,模型识别精度稳定在95%以上。YOLO算法历经从YOLOv1至YOLOv12的迭代演进,通过融合注意力机制、轻量化网络、特征金字塔等策略,将平均检测精度提升至90%～98%,同时将模型大小压缩至个位数MB量级,实现了精度与效率的协同优化。此外,本文分析了PlantVillage、AI Challenger 2018等公开数据集的不足,针对数据规范性和标注一致性问题提出改进建议。
Abstract:
Grape leaf diseases pose a severe threat to the sustainable development of the grape industry. Traditional manual diagnosis is plagued by strong subjectivity, low efficiency and poor timeliness, making it difficult to meet the development needs of modern agriculture. In the early stage, intelligent detection of grape leaf diseases was mainly based on traditional machine learning algorithms such as support vector machines, K-nearest neighbors and random forests, with the recognition accuracy gradually rising from 60% to 85%-90%. Convolutional neural networks have effectively addressed the challenge of multi-scale feature extraction through architectural innovations including Inception modules, residual connections and dense connections, enabling the model recognition accuracy to stabilize at over 95%. The YOLO algorithm has undergone iterative evolution from YOLOv1 to YOLOv12. By integrating strategies such as attention mechanisms, lightweight networks and feature pyramids, it has raised the mean detection accuracy to 90%-98% and simultaneously compressed the model size to the single-digit MB level, achieving the coordinated optimization of accuracy and efficiency. In addition, this paper analyzes the shortcomings of public datasets such as PlantVillage and AI Challenger 2018, and puts forward improvement suggestions for the problems of data standardization and annotation consistency.

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备注/Memo

备注/Memo:
收稿日期：2025-10-23基金项目：国家重点研发计划项目(2022YFD2100202、2018YFD0401404)作者简介：吴建军(1976-),男,河南温县人,博士,教授,研究方向为计算机科学与技术、粮食信息处理等。(E-mail)13939003632@163.com通讯作者：赵惠圆,(E-mail）1272887985@qq.com
更新日期/Last Update: 2026-05-11