Eff-3DPSeg: 3D Organ-Level Plant Shoot Segmentation Using Annotation-Efficient Deep Learning

作者：Liyi Luo, Xintong Jiang, Yang Yu, Eugene Roy Antony Samy, Mark Lefsrud, Valerio Hoyos‐Villegas, Shangpeng Sun · 发表于：Plant Phenomics · 年份：2023 · DOI：10.34133/plantphenomics.0080 · 被引用次数：48 · 研究领域：Smart Agriculture and AI、Remote Sensing and LiDAR Applications、Leaf Properties and Growth Measurement

Reliable and automated 3-dimensional (3D) plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Meshlab-based Plant Annotator was developed for plant point cloud annotation. Second, a weakly supervised deep learning method was proposed for plant organ segmentation. The method contained (a) pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds and (b) fine-tuning the pretrained model with about only 0.5% points being annotated to implement plant organ segmentation. After, 3 phenotypic traits (stem diameter, leaf width, and leaf length) were extracted. To test the generality of the proposed method, the public dataset Pheno4D was included in this study. Experimental results showed that the weakly supervised network obtained similar segmentation performance compared with the fully supervised setting. Our method achieved 95.1%, 96.6%, 95.8%, and 92.2% in the precision, recall, F...