Processing 3D Point Clouds for High-Throughput Plant Phenotyping

In recent years, measuring three-dimensional (3D) surface information has gained a great interest in plant phenotyping because it can represent the nature of plant architecture better than conventional 2D images. This paper presents an approach for processing 3D point clouds converted from 2D RGB images in the context of high-throughput plant phenotyping. High-resolution RGB multi-view imagery of a Chickpea plant was collected using a high-end camera. Based on these image sequences, 3D point cloud reconstruction of the canopy was conducted and analyzed. Later sophisticated 3D operations were performed on these images including 3D downsampling and after that clustering was performed on the processed point cloud. The information generated can help in the evaluation of crop traits and provide accurate statistics for the assessment of their growth parameters.