Robotic fruit monitoring is a key step toward automated agricultural production systems. Robots can significantly enhance plant and temporal fruit monitoring by providing precise, high-throughput assessments that overcome the limitations of traditional manual methods. Fruit monitoring is a challenging task due to the significant variation in size, shape, orientation, and occlusion of fruits. Also, fruits may be harvested or newly grown between recording sessions. Most methods are 2D image-based and they lack the 3D structure, depth, and spatial information, which represent key aspects of fruit monitoring. 3D colored point clouds, instead, can offer this information but they introduce challenges such as their sparsity and irregularity. In this paper, we present a novel approach for temporal fruit monitoring that addresses point clouds collected in a greenhouse over time. Our method segments fruits using a learning-based instance segmentation approach directly on the point cloud. Each segmented fruit is processed by a 3D sparse convolutional neural network to extract descriptors, which are used in an attention-based matching network to associate fruits with their instances from previous data collections. Experimental results on a real dataset of strawberries demonstrate that our approach outperforms other methods for fruits re-identification over time, allowing for precise temporal fruit monitoring in real and complex scenarios.

本研究解决了传统手动方法在果实监测中的局限性，特别是在果实尺寸、形状和遮挡方面的变化。我们提出了一种基于点云的实例分割方法，利用3D稀疏卷积神经网络进行果实特征提取，并通过注意力匹配网络进行果实的重新识别。实验结果表明，该方法在复杂场景中实现了更精确的时果监测效果，具有显著的应用潜力。

通过3D实例分割和点云重新识别进行园艺时果监测