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精确检测植物疾病的智能深度学习架构,促进农业新质量的生产力
Jun Liu1, Xuewei Wang1, Qian Chen2
1Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China.
Frontiers in plant science
|August 29, 2025
概括
这项研究引入了YOLO-vegetable,这是一种用于检测温室植物疾病的先进算法,在恶劣的照明和遮蔽等条件下显著提高了准确性. 它为精准农业提供了实用解决方案.
科学领域:
- 农业科学
- 计算机视觉
- 机器学习
背景情况:
- 温室植物疾病的检测面临挑战,包括不均的照明,目标封闭和混合感染.
- 现代精准农业需要强大而准确的疾病识别系统.
研究的目的:
- 开发一种高精度的温室植物疾病检测算法.
- 在复杂的环境条件下解决现有方法的局限性.
主要方法:
- 拟议的YOLO蔬菜算法基于改进的You Only Look Once版本10 (YOLOv10).
- 整合了自适应细节增强卷积 (ADEConv),多颗粒度特征融合检测层 (MFLayer) 和带有注意力引导的自适应特征选择的层间动态融合金字塔网络 (IDFNet).
- 在自建的15000张植物疾病数据集 (VDD) 上进行验证.
主要成果:
- 在0.5的IOU值下,YOLO植物实现了95.6%的平均精度 (mAP).
- 与基线模型相比显示了6. 4个百分点的改善.
- 保持每3.8M参数和18.6ms推断时间的计算效率.
结论:
- YOLO-vegetable为农业设施中的智能疾病检测提供了实用且高效的解决方案.
- 拟议的模块增强了细粒度的特征保存,小目标定位和关键信息提取.
- 通过改善疾病管理,促进农业新质量的生产力.
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