一种基于波形波段的频域方法,用于准确检测多种作物的疾病
Jiamu Zhao1, Yongchao Liang2, Gongmin Wei1
1College of Big Data and information engineering, Guizhou University, Guiyang, 550025, China.
Scientific reports
|February 3, 2026
概括
新的农作物疾病识别模型WGA-YOLO使用波形通道重新校准 (WCR) 和增强的卷积来准确检测田间病变. 它提供了提高效率和部署友好性,同时保持强大的性能.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的作物疾病诊断对于有效管理和减少农药使用至关重要.
- 现场病变检测是具有挑战性的,因为外观,尺寸,照明和阴影的变化.
- 现有的方法在实时应用中努力平衡高精度与轻量级推理.
研究的目的:
- 开发一种轻量级但准确的作物疾病识别模型,用于现场应用.
- 引入用于增强特征提取和多尺度上下文聚合的新型模块.
- 提高作物疾病检测系统的效率和部署友好性.
主要方法:
- 拟议的WGA-YOLO是一种轻量级的YOLO变体,采用波形通道重新校准 (WCR) 进行多分辨率特征融合.
- 引入了PS-C2f模块,具有Pinwheel形状的卷曲,用于捕捉微细的病变细节.
- 用动态集团注意力聚合 (DGAP) 取代了SPPF,以实现高效的多层次上下文聚合.
主要成果:
- 在PlantDoc_boost数据集上,WGA-YOLO的准确性比YOLOv8n高3.02%和2.85%.
- 与YOLOv8n.相比,模型参数减少了约0.18M,FLOP减少了约0.3G.
- 证明了卓越的推断效率和部署友好性.
结论:
- WGA-YOLO有效地解决了在田间检测作物疾病的挑战.
- 拟议的WCR和PS-C2f模块增强了特征表示和细节捕获.
- WGA-YOLO为实时,准确和高效的作物疾病识别提供了一个有前途的解决方案.
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