Research on Crop Fruit Segmentation Method Based on Point Cloud

The precise segmentation of point cloud is the basic work of 3D reconstruction, which has important value in scene understanding fields such as unmanned driving, crop phenotype measurement, fruit grading, picking robots and postharvest sorting. At present, there are many effective remedies to improve the speed and accuracy of segmentation on simple scenes. However, due to the complexity of real-world and agricultural environment and the limitations of 3D acquisition devices, it is still challenging to efficiently segment the target object from the chaotic real background. In this paper, we provide a comprehensive review of point cloud segmentation techniques, and classify point cloud segmentation methods based on different mathematical principles to provide insight to inspire further research. To address the problem that point cloud segmentation algorithms are difficult to evaluate, a quantitative evaluation scheme is proposed by combining segmentation precision, recall and F-score. Finally, we analyze the advantages and disadvantages of algorithms for application in the agricultural field through fruit point cloud segmentation experiments. At the same time, potential future research directions is inspired in this rapidly developing field.