Automated Phenotyping of Maize from 3D Point Clouds Using an Optimized Deep Learning Approach

Accurate plant organ segmentation and efficient phenotypic parameter acquisition remain major challenges in plant phenomics. This study develops an automated phenotyping framework for maize that integrates deep learning with 3D point cloud analysis to overcome the inefficiency and subjectivity of traditional manual methods. A high-quality 3D maize point cloud dataset was constructed, and a segmentation model named PSCSO was proposed based on the PointNet++ architecture. The model incorporates an SCConv module to reduce feature redundancy and uses the Sophia optimizer to improve convergence efficiency. Experimental results show the model achieved segmentation accuracies of 0.926 on the training set and 0.861 on the testing set, with a MIoU of 0.843, while significantly reducing training time. Based on the segmentation results, the model automatically estimates seven key phenotypic parameters: plant height, crown diameter, stem height, stem diameter, leaf length, leaf width, and leaf area. This is achieved by integrating point cloud algorithms including linear regression, PCA, and Delaunay triangulation. The predictions showed excellent agreement with manual measurements, with all parameters achieving R2 values exceeding 0.91. Overall, this automated framework provides a reliable and high-throughput solution for plant phenotypic analysis.