不同地面细分方法对基于无人机的树冠体积测量的准确性的影响
Leng Han1,2,3, Zhichong Wang4, Miao He1,2,3
1College of Science, China Agricultural University, Beijing, China.
Frontiers in plant science
|July 3, 2024
概括
无人驾驶飞行器 (UAV) 遥感与U-Net深度学习实现了精确的果树树体积测量. 这种方法超越了精确果园管理的传统技术,即使具有有限的标记数据.
科学领域:
- 农业工程 农业工程
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 果园的精确管理受到树冠分布不均的阻碍.
- 无人驾驶飞行器 (UAV) 遥感,利用运动结构 (SFM) 技术,为测量果园树冠特征提供了高效和准确的解决方案.
研究的目的:
- 开发和评估基于无人机的遥感管道,用于测量果树树的体积.
- 为了比较不同树冠细分方法 (U-Net,OTSU,RANSAC) 的精度来进行体积估计.
主要方法:
- 从无人机捕获的RGB图像中构建RGB和数字表面模型 (DSM) 的正视图.
- 使用U-Net,OTSU和RANSAC算法进行细分的果树树冠.
- 计算了树冠体积,并比较了细分和体积估计的准确性.
主要成果:
- 经过RGB和DSM训练的U-Net显示出优异的细分精度 (MIoU: 84.75%,MPA: 92.58%).
- 仅使用DSM进行训练的U-Net提供了最准确的体积估计 (RMSE:0.410 m3,rRMSE:6.40%,MAPE:4.74%).
- 与OTSU和RANSAC相比,基于深度学习的细分提高了这两项任务的准确性.
结论:
- U-Net细分为天花板体积测量提供了更高的准确性,特别是在手动标记的数据集.
- 分区OTSU为RANSAC提供了一个更准确的替代方案,用于在数据标签成本令人担忧时估计天花板体积.
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