TomatoPGT: A 3D point cloud dataset of tomato plants for segmentation and plant-trait extraction

Three-dimensional (3D) point-cloud phenotyping enables non-destructive and repeatable characterization of plant architecture, supporting the measurement of traits such as internode length, branching topology, and organ orientation. This article presents <b>TomatoPGT (Tomato Plant Graph Twin)</b>, a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction. The dataset contains <b>42 scans</b> from <b>three greenhouse-grown tomato plants</b> acquired across early to mid-vegetative development using a rotational multi-view imaging system. Each scan consists of <b>60-70 overlapping RGB images</b> captured under uniform illumination and reconstructed into a <b>metrically scaled dense colored point cloud</b> using Structure-from-Motion and multi-view stereo. TomatoPGT provides: (i) multi-view RGB images, (ii) dense colored point clouds, (iii) manually curated semantic and instance annotations at organ level, (iv) graph representations encoding plant topology and geometry, and (v) tabulated phenotypic traits computed deterministically from the graphs (internode length, insertion angles, and phyllotactic angles). TomatoPGT supports reproducible development and evaluation of 3D phenotyping pipelines, including learning-based segmentation and graph-based modeling of plant architecture.