Research on Plant Growth Tracking Based on Point Cloud Segmentation and Registration

Abstract

Plant phenotypic analysis is of great importance to the development of agricultural engineering, and is one of the core issues in crop science and plant breeding. Since plant growth is spatio-temporal and synchronous, understanding the growth and development of individual plants can help to reveal the growth potential of the whole plot and thus improve planting methods. In recent years, the technical means to analyze the growth situation using 3D point cloud data has received extensive attention. The plant point cloud obtained by scanning plants with LiDAR has the characteristics of high resolution, high precision, etc. Periodic scanning of the same plant for spatio-temporal point cloud data sets allows monitoring of growth through subtle changes of plant organs. Organ tracking of growing plants remains challenging due to the lightward nature of growth, the potential for topological changes and the unpredictability of plant growth over time, with the possibility of new leaf growth and leaf death. This paper designs a plant organ growth tracking method based on point cloud. First of all, for growing plants, we have established a crop point cloud spatio-temporal dataset based on two publicly available point cloud datasets. The data set includes four species, tomato, tobacco, sorghum and maize, each species contains complete organ instance labels, and each organ of the same plant has a unique label, which means that the labels of the same organ of an individual at different scan dates correspond one-to-one. Second, this paper proposes a point cloud data-based plant organ growth tracking method, which uses a cost correlation matrix to automatically track growing plant organs. Finally, based on the set of quantitative evaluation metrics, our algorithm achieves a matching accuracy of 82.89% on the plant spatio-temporal dataset and good growth tracking results in the qualitative analysis.