在葡萄园中使用叶绿素吸收指数和YOLO深度学习直接基于无人机的Botrytis cinerea检测
Guillem Montalban-Faet1, Enrique Pérez-Mateo1, Rafael Fayos-Jordan1
1Computer Science Department, ETSE-Universitat de València, 46100 Valencia, Spain.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究介绍了一种人工智能驱动的无人机系统,用于在葡萄园中早期检测Botrytis cinerea. 使用多谱图像和YOLOv8模型,它显著提高了精密葡萄种植的疾病检测准确性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 农业5.0需要智能系统来自主监测作物健康状况.
- 早期和准确的检测葡萄藤疾病如 Botrytis cinerea 对于产量保存至关重要.
研究的目的:
- 设计和评估基于AI的无人机系统,用于在葡萄园中检测Botrytis cinerea.
- 评估多光谱图像和深度学习用于自动疾病识别的有效性.
主要方法:
- 整合了校准的多谱数据,植被指数 (特别是CARI) 和YOLOv8物体检测模型.
- 在真实的葡萄园条件下对AI-UAV系统进行现场评估.
- 使用多光谱与RGB图像检测性能的比较.
主要成果:
- 卡里训练的模型实现了高检测性能:92.6%的精度,89.6%的回忆,91.1%的F1分数,93.9%的mAP@50.
- 多光谱图像显著优于RGB图像 (F1得分68.1%,mAP@50 68.5%).
- 该系统展示了近乎实时的操作,每张图像的平均推断时间低于50毫秒.
结论:
- 生理学知情的光谱特征选择 (CARI) 显著增强了早期的Botrytis cinerea检测.
- 拟议的UAV-AI框架适用于农业5.0.0中的精密葡萄种植应用.
- 该系统可实现自动化,地理定位的疾病检测,以改善葡萄园管理.
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