Classification of rice seed variety using point cloud data combined with deep learning

Rice variety selection and quality inspection are key links in rice planting. Compared with two-dimensional images, three-dimensional information on rice seeds shows the appearance characteristics of rice seeds more comprehensively and accurately. This study proposed a rice variety classification method using three-dimensional point cloud data of the surface of rice seeds combined with a deep learning network to achieve the rapid and accurate identification of rice varieties. First, a point cloud collection platform was set up with a Raytrix light field camera as the core to collect three-dimensional point cloud data on the surface of rice seeds; then, the collected point cloud was filled, filtered and smoothed; after that, the point cloud segmentation is based on the RANSAC algorithm, and the point cloud downsampling is based on a combination of random sampling algorithm and voxel grid filtering algorithm. Finally, the processed point cloud was input to the improved PointNet network for feature extraction and species classification. The improved PointNet network added a cross-level feature connection structure, made full use of features at different levels, and better extracted the surface structure features of rice seeds. After testing, the improved PointNet model had an average classification accuracy of 89.4% for eight varieties of rice, which was 1.2% higher than that of the PointNet model. The method proposed in this study combined deep learning and point cloud data to achieve the efficient classification of rice varieties.