1College of Information Engineering (College of Artificial Intelligence), Yangzhou University, Yangzhou,Jiangsu 225127, China
2College of Horticulture and Landscape Architecture, Yangzhou University,Yangzhou, Jiangsu 225009, China
3These authors contributed equally to this wor
| Received 10 Jun 2024 |
Accepted 22 Aug 2024 |
Published 16 Oct 2024 |
Plant phenotyping plays a pivotal role in observing and comprehending the growth and development of plants. In phenotyping, plant organ segmentation based on 3D point clouds has garnered increasing attention in recent years. However, using only the geometric relationship features of Euclidean space still cannot accurately segment and measure plants. To this end, we mine more geometric features and propose a segmentation network based on a multiview geometric graph encoder, called SN-MGGE. First, we construct a point cloud acquisition platform to obtain the cucumber seedling point cloud dataset, and employ CloudCompare software to annotate the point cloud data. The GGE module is then designed to generate the point features, including the geometric relationships and geometric shape structure, via a graph encoder over the Euclidean and hyperbolic spaces. Finally, the semantic segmentation results are obtained via a downsampling operation and multilayer perceptron. Extensive experiments on a cucumber seedling dataset clearly show that our proposed SN-MGGE network outperforms several mainstream segmentation networks (e.g., PointNet++, AGConv, and PointMLP), achieving mIoU and OA values of 94.90% and 97.43%, respectively. On the basis of the segmentation results, 4 phenotypic parameters (i.e., plant height, leaf length, leaf width, and leaf area) are extracted through the K-means clustering method; these parameters are very close to the ground truth, and the R2 values reach 0.98, 0.96, 0.97, and 0.97, respectively. Furthermore, an ablation study and a generalization experiment also show that the SN-MGGE network is robust and extensive.
