用深度学习分割橄榄树冠与影子，估算生物量以支持精准农业。
Comparative Analysis of Deep Learning Models for Olive Tree Crown and Shadow Segmentation Towards Biovolume Estimation
- 对比U-Net、YOLOv11m-seg、Mask R-CNN三模型在无人机影像中分割树冠与影子。
- Mask R-CNN准确率最高（F1=0.86，mIoU=0.72），YOLOv11m-seg推理最快（0.12秒/图）。
- 结果可用于大范围果园监测，适合不同场景下的生物量估算需求。
橄榄树生物量估算是精准农业中的关键任务，有助于产量预测与资源管理，尤其在受气候变化影响严重的地中海地区。本研究对三种深度学习模型——U-Net、YOLOv11m-seg和Mask R-CNN——在超高清无人机影像中分割橄榄树冠及其阴影进行了比较分析。数据集来自意大利维科皮萨诺，包含人工标注的树冠与阴影掩码。基于这些标注，方法强调空间特征提取与鲁棒分割；每棵树的生物量通过结合冠层投影面积与阴影推导的高度（利用太阳几何关系）进行估算。测试结果显示，Mask R-CNN总体精度最高（F1 = 0.86，mIoU = 0.72），而YOLOv11m-seg推理速度最快（0.12秒/图像）。估算的生物量范围约为4至24立方米，反映了树体结构差异。研究表明，当生物量精度优先时推荐使用Mask R-CNN，大规模部署时则可选用快速的YOLOv11m-seg；U-Net作为轻量级高敏感模型仍具应用价值。该框架支持高精度、可扩展的果园监测，未来可通过融合数字高程模型（DEM）或数字表面模型（DSM）及田间校准，进一步提升其在实际决策中的支持能力。
原文摘要 · Abstract (English)
Olive tree biovolume estimation is a key task in precision agriculture, supporting yield prediction and resource management, especially in Mediterranean regions severely impacted by climate-induced stress. This study presents a comparative analysis of three deep learning models U-Net, YOLOv11m-seg, and Mask RCNN for segmenting olive tree crowns and their shadows in ultra-high resolution UAV imagery. The UAV dataset, acquired over Vicopisano, Italy, includes manually annotated crown and shadow masks. Building on these annotations, the methodology emphasizes spatial feature extraction and robust segmentation; per-tree biovolume is then estimated by combining crown projected area with shadow-derived height using solar geometry. In testing, Mask R-CNN achieved the best overall accuracy (F1 = 0.86; mIoU = 0.72), while YOLOv11m-seg provided the fastest throughput (0.12 second per image). The estimated biovolumes spanned from approximately 4 to 24 cubic meters, reflecting clear structural differences among trees. These results indicate Mask R-CNN is preferable when biovolume accuracy is paramount, whereas YOLOv11m-seg suits large-area deployments where speed is critical; U-Net remains a lightweight, high-sensitivity option. The framework enables accurate, scalable orchard monitoring and can be further strengthened with DEM or DSM integration and field calibration for operational decision support.
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