农业中小物体检测:使用EN-YOLO和热融合对杜利亚果园的案例研究
Ruipeng Tang1, Tan Jun2, Qiushi Chu3
1School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK.
Plants (Basel, Switzerland)
|September 13, 2025
概括
一个新的深度学习模型,EN-YOLO,使用多式成像技术准确检测榴害虫和疾病. 这种自动化系统通过提高检测准确性和可扩展性来增强智能农业.
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:
- 东南亚的榴莲生产面临着由于害虫和疾病而造成的大量产量和质量损失.
- 目前手工检查榴害虫和疾病的方法是劳动密集型,不准确,难以扩展.
研究的目的:
- 开发一个增强的深度学习模型 (EN-YOLO),用于精确和自动检测榴莲害虫和疾病.
- 为了提高在榴莲种植中检测害虫和疾病的准确性,稳定性和可扩展性.
主要方法:
- 拟议的EN-YOLO模型整合了EfficientNet骨干和多式联络注意力机制.
- 使用多式输入:RGB,近红外和热成像,以提高强度.
- 通过删除多余层并添加大跨度剩余边缘来优化模型架构.
主要成果:
- 与YOLOv8,YOLOv5-EB和Fieldsentinel-YOLO相比,EN-YOLO实现了更高的检测准确度,概括性和小物体识别.
- 显示了95.3%的计数精度和在切除和交叉场景测试中的强大性能.
- 该系统支持实时无人机部署,并集成了专家知识库来支持决策.
结论:
- 在智能农业中,EN-YOLO为自动化害虫和疾病管理提供了一种高效,可解释和可扩展的解决方案.
- 多式联网方法在具有挑战性的环境条件下提高了检测可靠性.
- 这项技术有助于智能决策,实现可持续榴莲种植.
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