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.

翻译：精确的作物计数对于有效的农业管理和明智的干预策略至关重要。然而，在室外田间环境中，部分遮挡与从单一视角区分聚集作物时固有的模糊性相结合，对基于图像的分割方法构成了巨大挑战。为解决这些问题，我们引入了一种新颖的作物计数框架，旨在通过三维实例分割实现精确计数。我们的方法利用从多个视角捕获的二维图像，并将独立的实例掩码与用于神经辐射场视图合成的过程相关联。我们引入了作物可见性和掩码一致性分数，这些分数与来自NeRF模型的三维信息相结合。这实现了作物实例在三维空间中的有效分割和高度精确的作物计数。此外，我们的方法消除了对作物特定参数调整的依赖。我们在包含棉铃、苹果和梨的三个农业数据集上验证了我们的框架，并证明了尽管作物颜色、形状和尺寸存在显著差异，仍能保持一致的计数性能。与现有技术的对比分析突显了其在作物计数任务上的优越性能。最后，我们贡献了一个棉花植株数据集，以推动该主题的进一步研究。