PSCDR-BMPNet: A Point-Supervised Contrastive Deep Regression Network for Point Cloud Biomass Prediction
Source abstract
Abstract
Accurate assessment of above-ground biomass (AGB) is essential for optimizing crop growth and enhancing agricultural efficiency. However, predicting above-ground biomass (AGB) presents significant challenges. Traditional point cloud networks often struggle with processing crop structures and data characteristics, hindering their ability to predict biomass accurately. To address these limitations, we propose a point-supervised contrastive deep regression method (PSCDR) and a novel network, BMP_Net (BioMixerPoint_Net). The PSCDR method leverages the benefits of deep contrastive regression while accounting for the modal differences between point cloud and 2D image data. By incorporating Chamfer distance to measure point cloud similarity, it improves the model’s adaptability to point cloud features. Experimental results on the SGCBP public dataset show that PSCDR significantly reduces prediction errors compared to seven other point cloud models. Furthermore, the BMP_Net network, which integrates the novel PFMixer module and a point cloud downsampling module, effectively captures the relationship between point cloud structure, density, and biomass. The model achieved test results with RMSE, MAE, and MAPE values of 75.92, 63.19, and 0.115, respectively, outperforming PointMixer by 37.94, 30.07, and 0.079. This method provides an efficient biomass monitoring tool for precision agriculture.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage · Forest Ecology and Biodiversity Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Funding
National Key Research and Development Program of China
References (40)
Show source-provided references
1. Guo, Y., He, J., Zhang, H., Shi, Z., Wei, P., Jing, Y., Yang, X., Zhang, Y., Wang, L., and Zheng, G. (2024). Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images. Agriculture, 14.10.3390/agriculture14030378 ↗
2. Yang, Z., Yu, Z., Wang, X., Yan, W., Sun, S., Feng, M., Sun, J., Su, P., Sun, X., and Wang, Z. (2024). Estimation of Millet Aboveground Biomass Utilizing Multi-Source UAV Image Feature Fusion. Agronomy, 14.10.3390/agronomy14040701 ↗
3. Estimation of Aboveground Biomass and Chlorophyll Content in Different Varieties of Alfalfa Based on UAV Multispectral Data
4. Improving Potato Above Ground Biomass Estimation Combining Hyperspectral Data and Harmonic Decomposition Techniques10.1016/j.compag.2024.108699 ↗
5. Wang, F., Wang, J., Ji, Y., Zhao, B., Liu, Y., Jiang, H., and Mao, W. (2023). Research on the Measurement Method of Feeding Rate in Silage Harvester Based on Components Power Data. Agriculture, 13.10.3390/agriculture13020391 ↗
6. Classification Method of Wheat Harvester Feeding Density Based on MobileViT Model
7. Singh, B., Kumar, S., Elangovan, A., Vasht, D., Arya, S., Duc, N.T., Swami, P., Pawar, G.S., Raju, D., and Krishna, H. (2023). Phenomics Based Prediction of Plant Biomass and Leaf Area in Wheat Using Machine Learning Approaches. Front. Plant Sci., 14.10.3389/fpls.2023.1214801 ↗
8. Elangovan, A., Duc, N.T., Raju, D., Kumar, S., Singh, B., Vishwakarma, C., Krishnan, S.G., Ellur, R.K., Dalal, M., and Swain, P. (2023). Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice. Agriculture, 13.10.3390/agriculture13040852 ↗
9. A Comprehensive Analysis of Deep Regression
10. Zhang, J., Zhou, C., Zhang, G., Yang, Z., Pang, Z., and Luo, Y. (2024). A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning. Forests, 15.10.3390/f15030456 ↗
11. Vahidi, M., Shafian, S., Thomas, S., and Maguire, R. (2023). Pasture Biomass Estimation Using Ultra-High-Resolution RGB UAVs Images and Deep Learning. Remote Sens., 15.10.3390/rs15245714 ↗
12. Yang, Y.Z., Zha, K.W., Chen, Y.C., Wang, H., and Katabi, D. (2021, January 8–24). Delving into Deep Imbalanced Regression. Proceedings of the 38th International Conference on Machine Learning, Vienna, Austria.
13. Wang, Z., and Wang, H. (2023, January 10–16). Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing. Proceedings of the 37th Conference on Neural Information Processing Systems, New Orleans, LA, USA.
14. Deep Neural Networks for Rank-Consistent Ordinal Regression Based on Conditional Probabilities10.1007/s10044-023-01181-9 ↗
15. Wang, Y., Jiang, Y., Li, J., Ni, B., Dai, W., Li, C., Xiong, H., and Li, T. (2022, January 19–24). Contrastive Regression for Domain Adaptation on Gaze Estimation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.01877 ↗
16. Rank-N-Contrast: Learning Continuous Representations for Regression
17. Keramati, M., Meng, L., and Evans, R.D. (2023). ConR: Contrastive Regularizer for Deep Imbalanced Regression. arXiv.
18. He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, TX, USA.
19. Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y. (2018). Learning Deep Representations by Mutual Information Estimation and Maximization. arXiv.
20. Oord, A., Li, Y., and Vinyals, O. (2018). Representation Learning with Contrastive Predictive Coding. arXiv.
21. Vinodkumar, P.K., Karabulut, D., Avots, E., Ozcinar, C., and Anbarjafari, G. (2023). A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds. Entropy, 25.10.3390/e25040635 ↗
22. Destructive and Non-Destructive Measurement Approaches and the Application of AI Models in Precision Agriculture: A Review10.1007/s11119-024-10112-5 ↗
23. Debnath, S., Paul, M., and Debnath, T. (2023). Applications of LiDAR in Agriculture and Future Research Directions. J. Imaging, 9.10.3390/jimaging9030057 ↗
24. LiDAR Applications in Precision Agriculture for Cultivating Crops: A Review of Recent Advances10.1016/j.compag.2023.107737 ↗
25. Revenga, J.C., Trepekli, K., Oehmcke, S., Jensen, R., Li, L., Igel, C., Gieseke, F.C., and Friborg, T. (2022). Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods. Remote Sens., 14.10.3390/rs14163912 ↗
26. Biomass Estimation and Uncertainty Quantification from Tree Height10.1109/JSTARS.2023.3271186 ↗
27. Colaco, A.F., Schaefer, M., and Bramley, R.G.V. (2021). Broadacre Mapping of Wheat Biomass Using Ground-Based LiDAR Technology. Remote Sens., 13.10.3390/rs13163218 ↗
28. Ma, J., Zhang, W., Ji, Y., Huang, J., Huang, G., and Wang, L. (2023). Total and Component Forest Aboveground Biomass Inversion via LiDAR-Derived Features and Machine Learning Algorithms. Front. Plant Sci., 14.10.3389/fpls.2023.1258521 ↗
29. Modelling Tree Biomass Using Direct and Additive Methods with Point Cloud Deep Learning in a Temperate Mixed Forest10.1016/j.srs.2023.100110 ↗
30. Pan, L., Liu, L., Condon, A.G., Estavillo, G.M., Coe, R.A., Bull, G., Stone, E.A., Petersson, L., and Rolland, V. (2022, January 3–8). Biomass Prediction with 3D Point Clouds from LiDAR. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.10.1109/WACV51458.2022.00178 ↗
31. Deep Point Cloud Regression for Above-Ground Forest Biomass Estimation from Airborne LiDAR10.1016/j.rse.2023.113968 ↗
32. Lei, Y., and Ma, H. (2022, January 25–27). BioPM: Mixer for Point Cloud Based Biomass Prediction. Proceedings of the 41st Chinese Control Conference, Hefei, China.10.23919/CCC55666.2022.9902033 ↗
33. Oehmcke, S., Li, L., Revenga, J.C., Nord-Larsen, T., Trepekli, K., Gieseke, F., and Igel, C. (2022, January 1–4). Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass. Proceedings of the 30th International Conference on Advances in Geographic Information Systems, Seattle, WA, USA.10.1145/3557915.3561471 ↗
34. Wu, C.Z., Zheng, J.W., Pfrommer, J., and Beyerer, J. (2023, January 17–24). Attention-Based Point Cloud Edge Sampling. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00516 ↗
35. Choe, J., Park, C., and Rameau, F. (2022, January 23–27). Pointmixer: Mlp-mixer for point cloud understanding. Proceedings of the 17th European Conference on Computer Vision, Tel Aviv, Israel.10.1007/978-3-031-19812-0_36 ↗
36. Zhao, H.S., Jiang, L., Jia, J.Y., Torr, P.H.S., and Koltun, V. (2021, January 11–17). Point Transformer. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada.10.1109/ICCV48922.2021.01595 ↗
37. PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
38. Liang, D.K., Zhou, X., Xu, W., Zhu, X.K., Zou, Z.K., Ye, X.Q., Tan, X., and Bai, X. (2024). PointMamba: A Simple State Space Model for Point Cloud Analysis. arXiv.
39. Li, X., Hayder, Z., Zia, A., Cassidy, C., Liu, S., Stiller, W., Stone, E., Conaty, W., Petersson, L., and Rolland, V. (2024). NeFF-BioNet: Crop Biomass Prediction from Point Cloud to Drone Imagery. arXiv.10.1109/WACV61041.2025.00754 ↗
40. Completing 3D Point Clouds of Individual Trees Using Deep Learning10.1111/2041-210X.14412 ↗