整合无人机衍生的直径估计和机器学习,精确地绘制白菜产量图
Sara Tokhi Arab1, Akane Takezaki2, Masayuki Kogoshi1
1Research Center for Agricultural Robotics, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0856, Japan.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
使用深度学习的无人机 (UAV) 图像准确估计了白菜头直径,并预测了产量. 这种由人工智能驱动的框架为传统农业方法提供了一个非破坏性的,精确的替代方案.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 传统的白菜产量估计是劳动密集型和耗时的.
- 无人机 (UAV) 图像提供了更高效和空间意识的方法.
- 评估空间可变性有助于资源分配和可变率应用.
研究的目的:
- 开发一种非破坏性的方法,用无人机成像来估计白菜头径.
- 用估计直径和环境数据来预测单个白菜头的新鲜重量.
- 评估深度学习和机器学习模型对精准农业的性能.
主要方法:
- 用高分辨率RGB无人机图像的YOLOv8s-pose和YOLOv11s-pose深度学习模型估计了个别的白菜头直径.
- 从多谱无人机图像中提取了气候变量 (温度,降雨) 和天花板反射率指数 (NDVI,NDRE,CIg).
- 机器学习模型,包括CatBoost,使用直径估计和环境数据来预测新鲜重量.
主要成果:
- YOLOv11s-pose显示直径估计的高精度,平均相对误差为4.6%,mAP为98.5%.
- CatBoost在新鲜体重预测中获得了最低的平均二次误差 (0.025 kg/卷心菜) 和最高的R2 (0.89).
- 综合人工智能框架显著提高了非侵入性收益率估计的准确性.
结论:
- 基于深度学习的姿势估计与无人机图像提供了准确的非破坏性的白菜直径测量.
- 结合直径,气候和光谱数据的人工智能支持的框架提高了白菜种植的精确产量预测.
- 这种方法支持有效的资源管理和农业供应链优化.
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