DUFA-Net: A Deep Learning-Based Method for Organ-Level Segmentation and Phenotype Extraction of Maize 3D Point Clouds

Accurate plant phenotyping is crucial for gaining a deeper understanding of plant growth patterns and improving yield. However, the segmentation and measurement of 3D phenotypic data in maize remains challenging due to factors such as complex canopy structure, occlusion, and uneven point distribution. To address this, we propose a deep learning network, DUFA-Net, based on dual uncertainty-driven feature aggregation. This method employs a dual uncertainty-driven farthest point sampling (DU-FPS) strategy to mitigate errors caused by uneven point cloud density. Furthermore, for local feature encoding, we designed a Dynamic Feature Aggregation (DFA) module to model neighborhood structures and capture fine-grained geometric features, thereby effectively handling complex canopy structures. Experiments on a self-constructed maize dataset demonstrate that DUFA-Net achieves 95.82% segmentation accuracy and a mean IoU of 92.52%. Based on the segmentation results, six key phenotypic features were accurately extracted, showing high R2 values ranging from 0.92 to 0.99. Further evaluation on the Syau Single Maize dataset confirms the generalization capability of the proposed method, achieving 92.52% accuracy and 91.23% mIoU, outperforming five state-of-the-art baselines, including PointNet++, PointMLP, and CurveNet. These results highlight the effectiveness and robustness of DUFA-Net for high-precision organ segmentation and phenotypic trait extraction in complex plant architectures.