## 4 Discussion
### Technical Contributions and Result Interpretation  
To address the limitations of traditional point cloud networks in biomass prediction tasks, particularly their insufficient handling of crop structural information and the challenge of uncovering the relationship between point cloud characteristics and biomass, this study proposes the innovative PSCDR method and the BMP_Net network. [PERSON] et al. [25] employed extremely randomized tree regression, relying solely on statistical features of point cloud height while overlooking the nonlinear impact of crop canopy structure on biomass. Unlike general contrastive learning methods, such as MoCo by [PERSON] et al. [18] and ConR [17], PSCDR introduces the Chamfer distance metric into the contrastive loss function for the first time, directly quantifying the three-dimensional spatial similarity of point clouds. The incorporation of Chamfer distance significantly enhances the model's robustness to point cloud deformations. Specifically, PSCDR reduces the RMSE of the PointMixer from 113.41 to 102.11, outperforming ConR [17], which relies solely on label similarity.  
The PFMixer module in BMP_Net enhances local feature interactions through intra-set mixing and captures cross-regional correlations via inter-set mixing. Our ablation studies demonstrate that removing the PFMixer module increases the RMSE from 75.92 to 160.76, underscoring its critical role in noise suppression and feature fusion. This design surpasses the attention fusion block in BioNet [30], which depends solely on global attention, as PFMixer preserves multi-scale information through residual connections.  
Traditional downsampling methods often lead to the loss of key biomass-related features. For instance, BioNet by [PERSON] et al. [30] suffers from excessive dilution of wheat ear point clouds due to a fixed sampling rate. In contrast, the PTransDown module in BMP_Net prioritizes the retention of dense regions, such as stems and ears, through channel contraction and Softmax normalization. Our ablation experiments reveal that removing this module increases the MAE from 63.19 to 151.04, confirming its importance in preserving critical structural information.  
The N2P Attention mechanism enhances sensitivity to fine structures in point clouds by capturing information from the neighbors of each point and applying neighborhood attention weights, enabling the identification of spatial distribution patterns of wheat tillering nodes. With the introduction of N2P Attention, the network more effectively leverages the correlations within point cloud data, allowing for more accurate biomass prediction while maintaining efficiency and flexibility when handling large-scale, complex point cloud datasets.  
At the technical level, this study overcomes the modality adaptation bottleneck of point cloud data in regression tasks by developing a high-precision prediction framework through contrastive learning and structured module design. Compared to BioNet by [PERSON] et al. [30], which relies on attention fusion blocks, and BioPM by [PERSON] et al. [32], which focuses on crop growth characteristics, the proposed PSCDR method and BMP_Net architecture achieve deep exploration of point cloud geometric features through modality-adapted contrastive loss and structured module design. This work establishes a methodological foundation for point cloud-based biomass prediction. The reduction in RMSE from 84.48 to 75.92 with the incorporation of PSCDR represents a relative improvement of approximately 10.1%. This level of enhancement in prediction accuracy can be significant in practical agricultural scenarios, for instance, by enabling more precise variable-rate fertilization or more reliable yield estimations.