用3D神经辐射场实现精准作物计数，解决遮挡与密集排列难题。
CropNeRF: A Neural Radiance Field-Based Framework for Crop Counting
- 结合多视角图像与NeRF，通过可见性与掩码一致性实现3D实例分割。
- 在棉花、苹果、梨三类数据集上计数误差低于5%，且不依赖特定参数调优。
- 适合农业监测、智能农作系统研发者使用，尤其适用于密集种植场景。
精确的作物计数对有效农业管理与科学干预策略至关重要。然而，在户外田间环境中，部分遮挡以及从单一视角难以区分密集分布作物个体的问题，给基于图像的分割方法带来了巨大挑战。为此，我们提出一种基于神经辐射场（NeRF）的作物计数新框架，通过多视角2D图像实现精确的3D实例分割与计数。该方法利用多视角图像生成独立实例掩码，并融合NeRF提供的3D结构信息，引入作物可见性评分与掩码一致性评分，显著提升3D空间中作物实例的分割精度与计数准确性。此外，本方法无需针对特定作物调整参数。我们在包含棉花铃、苹果和梨的三个农业数据集上验证了框架性能，结果表明在作物颜色、形状和大小差异显著的情况下仍保持稳定高精度计数。与现有最优方法对比，本方法在多个指标上表现更优。最后，我们公开了一个棉花植株数据集，以推动该领域的进一步研究。
原文摘要 · Abstract (English)
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing clustered crops from individual viewpoints poses an immense challenge for image-based segmentation methods. To address these problems, we introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation. Our approach utilizes 2D images captured from multiple viewpoints and associates independent instance masks for neural radiance field (NeRF) view synthesis. We introduce crop visibility and mask consistency scores, which are incorporated alongside 3D information from a NeRF model. This results in an effective segmentation of crop instances in 3D and highly-accurate crop counts. Furthermore, our method eliminates the dependence on crop-specific parameter tuning. We validate our framework on three agricultural datasets consisting of cotton bolls, apples, and pears, and demonstrate consistent counting performance despite major variations in crop color, shape, and size. A comparative analysis against the state of the art highlights superior performance on crop counting tasks. Lastly, we contribute a cotton plant dataset to advance further research on this topic.
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