结合标准化的生长类评估,无人机传感器数据,地理信息系统处理和机器学习分类来得出与葡萄藤的活力和树冠体积的相关性
Ronald P Dillner1, Maria A Wimmer2, Matthias Porten1
1Department of Viticulture and Oenology, DLR (Dienstleistungszentrum Ländlicher Raum) Mosel/Steillagenzentrum, Gartenstraße 18, 54470 Bernkastel-Kues, Germany.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
机器学习模型使用多谱无人机 (UAV) 数据准确评估了葡萄树的活力. 这项技术有助于葡萄园管理,并自动化收获和修剪等任务.
科学领域:
- 农业工程 农业工程
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 准确评估葡萄藤活力对于有效的葡萄园管理和葡萄种植机械的自动化至关重要.
- 现有的方法可能缺乏自动化系统所需的精度,需要先进的数据驱动方法.
研究的目的:
- 开发和评估机器学习 (ML) 分类器,以使用多谱无人机 (UAV) 传感器数据预测葡萄藤生长类.
- 整合光谱,结构和纹理特征,以提高精密葡萄种植中的分类准确性.
主要方法:
- 利用多光谱无人机数据来生成光谱,结构 (来自数字地形/表面模型) 和纹理 (GLCM) 特性.
- 采用基于像素和对象的图像细分技术创建葡萄树排面罩以提取树冠独家特征.
- 训练随机森林分类器 (RFC) 和支持矢量机器 (SVM) 模型,通过网格搜索和交叉验证优化超参数.
主要成果:
- 机器学习模型成功地以高准确度预测了葡萄藤生长类.
- 特性重要性分析表明,光谱,结构和纹理数据对活力评估的重要贡献.
- 优化模型在分类不同增长阶段方面表现强.
结论:
- 基于ML的多光谱无人机数据分析提供了一种可靠的方法来评估葡萄藤的活力.
- 这种方法支持精密葡萄种植,通过实现自动化和准确的葡萄园管理决策来支持精密葡萄种植.
- 该方法提供了一个可扩展的解决方案,用于监测葡萄藤的健康状况并优化农业实践.
关键词:
地理信息系统 (GIS) 是指地理信息系统.无人机 (无人驾驶飞行器) 是一种无人驾驶飞行器.处理地理数据的过程.基本的真理 基本的真理机器学习是机器学习.多光谱图像处理技术摄影计量 (摄影计量) 是一种摄影仪.精密葡萄种植的葡萄种植.远程传感是一种遥感技术.葡萄树有活力,有力量.更多相关视频
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