Sementing the Field of Rapeseed from 3D Laser Point Cloud Using Deep Learning

Abstract

Wisdom agriculture is a significant stage goal in the process of agricultural modernization development. Wisdom agriculture promotes the integration of agricultural informatization and intelligence. In recent years, the new models of intelligent agriculture based on artificial intelligence has developed rapidly. In this paper, 3D laser point cloud is used as research data to carry out in-depth research in the field of agriculture based on deep learning technology and point cloud. In this study, the deep learning model Pointnet ++ was used to segment the rapeseed point cloud data in the field: (1) The color enhancement algorithm of HSV color space was used to achieve color threshold segmentation of rapeseed crop point cloud data in complex field environment, and Statistical Outlier Filter and Super-Voxel Clustering were used to segment group rapeseed point cloud respectively. Finally, two groups of pure rapeseed point cloud data were obtained. (2) In this research, six original rapeseed point cloud data sets were used as datasets to train and test the segmentation performance of Pointnet++ (Multi-scale Grouping, MSG) deep learning model for rapeseed point cloud. Intersection over Union(IoU) was taken as the evaluation index of point cloud segmentation accuracy. The IoU of rape point cloud data processed by the three segmentation methods were 0.7748, 0.8019 and 0.8260, respectively. The results show that the segmentation performance of the deep learning model based on Pointnet ++ (MSG) is higher than that of the conventional point cloud segmentation algorithm. Compared with the conventional point cloud segmentation models, the point cloud segmentation based on deep learning framework shows better performance. The construction of a deep learning framework for crop point cloud segmentation and classification in the field requires the corresponding feature extraction processing based on the geometric structure or attributes of specific crops. In the context of the rapid development of agricultural big data, the deep learning framework in the field of agriculture is robust to deal with complex field environment, and the application of deep learning to agricultural research has a good prospect.