DRP-Net and clustering algorithm for Stem-Leaf segmentation and phenotypic trait extraction from tomato point clouds

Abstract

Tomatoes are a globally important horticultural crop, and their high-yield, high-quality breeding relies on high-throughput, precise phenotyping. While 3D point cloud technology offers a new avenue for non-destructive plant phenotyping, the inherent complexity of tomato plant organ morphology and growth dynamics poses a significant challenge to existing segmentation methods. To address this, this study employed multi-view RGB image reconstruction to cost-effectively acquire high-quality point cloud data from four growth cycles. Based on the characteristics of our data, we adapted and proposed a hybrid dual-path downsampling method (HDPD) for dataset augmentation, and constructed a dynamic reference point propagation network (DRP-Net) for semantic segmentation. The DRP-Net architecture addresses geometric feature mismatches between organs through a dynamic kernel edge convolution module (DKEC). Furthermore, it utilizes a global–local semantic feature fusion upsampling module (GL-SFFU) to overcome boundary blurring caused by plant growth and enhance detail discrimination. Based on the semantic segmentation results, a clustering algorithm was used to achieve leaf instance segmentation and extract key phenotypic parameters. Experimental results demonstrate that DRP-Net achieves significant performance in the tomato stem and leaf segmentation task, with mean precision, recall, F1 score, and mIoU reaching 94.97%, 93.93%, 94.43%, and 89.34%, respectively. The extracted phenotypic parameters, such as leaf length, leaf width, and leaf area, exhibit strong correlations with manual measurements (R² greater than 0.92 and 0.88, respectively). This study provides an effective technical solution for the precise segmentation of complex plant organs and high-throughput phenotyping analysis for breeding.

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