## 5 Conclusions  
This paper focused on the task of biomass prediction using point cloud data. Compared to image data, point cloud data offers more comprehensive and detailed spatial information and structural features. To better extract the spatial feature information from point cloud data, we propose a contrastive learning-based method called PSCDR, which introduces Chamfer distance as a new feature metric. Additionally, we propose a deep regression network, BMP_Net, for point cloud biomass prediction tasks. BMP_Net integrates the PFMixer module and Point Cloud Transition Downsampling module, utilizing multiple cross-level residual connections for information exchange to fully explore the relationship between point cloud structure, point cloud density, and the corresponding biomass. On this foundation, the N2P Attention mechanism is introduced to capture and fuse the neighboring point information within the point cloud.  
Experiments on the SGCBP public dataset showed that BMP_Net alone achieved RMSE, MAE, and MAPE values of 84.48, 67.31, and 0.131, respectively, outperforming BioNet. After incorporating the PSCDR method, these three key performance metrics are optimized to RMSE = 75.92, MAE = 63.19, and MAPE = 0.115, demonstrating that the method significantly improves prediction accuracy. Furthermore, in experiments with different point cloud networks, adding the PSCDR method leads to performance improvements across all cases, further validating the general applicability and effectiveness of the PSCDR approach. The improved BMP_Net model achieves high recognition accuracy, making it suitable for precise above-ground biomass detection, thus providing strong technical support for research in related fields.  
However, this study has certain limitations. The dataset used lacks sufficient diversity in crop types, geographic regions, and growing conditions, which could help enhance the model's generalization capabilities. Additionally, there is room for improvement in the model's robustness in complex environments and its computational efficiency. Future research will focus on optimizing the model structure and algorithms, expanding the dataset's size and diversity, exploring more effective feature extraction and fusion strategies, and strengthening research on model interpretability. These efforts aim to promote the broader application of point cloud data in biomass prediction, providing more accurate and reliable technical support for precision agriculture and ecosystem research. While the improved BMP_Net model achieves high recognition accuracy on the SGCBP dataset, making it suitable for precise biomass detection of wheat and barley under similar conditions, its broader applicability to other crops and regions remains a key area for future investigation.  
Conceptualization, Y.W. and H.P.; methodology, Y.W. and H.P.; software, R.Z.; validation, H.P. and C.O.; data curation, M.T. and W.H.; writing--original draft preparation, Y.W.; writing--review and editing, H.P.; funding acquisition and supervision, P.J. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Key Research and Development Program of China, with the sub-topic of \"Research on Key Common Technology and System Development for Intelligent Harvesting of Specialty Cash Crops\" (No. 2022 YFD2002001), which is under the key project of \"Research and System Development of Key Common Technology for Intelligent Harvesting of Specialized Cash Crops\".  
Not applicable.  
The data is available on request because of privacy concerns.  
The authors declare no conflicts of interest.