Three-dimensional reconstruction and phenotypic identification of the wheat plant using RealSense D455 sensor

Accurate and rapid wheat morphology reconstruction and trait collection are essential for selecting varieties, scientific cultivation, and precise management. A single perspective is limited by environmental obstructions, hindering the collection of high-throughput phenotype data for wheat plants. Therefore, a rapid reconstruction method of multi-view three-dimensional point cloud is proposed to realize the high-throughput and accurate identification of wheat phenotype. Firstly, taking wheat at the tillering stage as the experimental object, a multi-view acquisition system based on a RealSense sensor was constructed, and the point cloud data of wheat were obtained from 16 views. Secondly, a joint photometric and geometric objective was optimized, and space location was registered by colored Point Cloud Registration (colored) and Iterative Closest Point (ICP) algorithms. Furthermore, the Multiple View Stereo (MVS) algorithm was used to combine the depth image, RGB image, and spatial position obtained by coarse registration to enable the fine registration of multi-viewpoint clouds. Compared with the traditional Structure From Motion (SFM)-MVS algorithm, our proposed method is much faster, with an average reconstruction time of 33.82 s. Moreover, the wheat plant height, leaf length, leaf width, leaf area, and leaf angle of wheat were calculated based on the three-dimensional point cloud of the wheat plant. The experimental results showed that the determination coefficients of the method are 0.996, 0.958, 0.956, 0.984, and 0.849, respectively. Finally, phenotypic information such as compact degree, convex hull volume, and average leaf area of different wheat varieties was analyzed and identified, proving that the method could capture the phenotypic differences between varieties and individuals. The proposed method provides a rapid approach to quantify wheat phenotypic traits, aiding breeding, scientific cultivation, and environmental management.