<p>The quality of forage point cloud data provides an important foundation for precision pasture management. Due to its high accuracy and real-time performance, LiDAR technology has become a potential tool for achieving fine management of forage. However, most existing mainstream point cloud processing algorithms are designed for structured or urban scenes. In natural pasture environments, where grass stems are dense, postures are complex, the ground is irregular, and noise points are widespread, traditional methods show significant shortcomings in registration, denoising, and segmentation, especially in ground extraction and grass segmentation. Therefore, a more targeted and robust preprocessing scheme is urgently needed. In this study, a dedicated point cloud preprocessing method for forage scenarios is proposed. First, an improved Weighted ICP (W-ICP) algorithm is used for data registration to obtain a complete and coherent point cloud model. Then, noise is removed by combining multi-level filtering and deep learning-based denoising. Next, an improved DBSCAN algorithm is employed to segment the data and extract the forage regions. Finally, feature extraction and classification are carried out by introducing an attention mechanism and a feature enhancement module into the PointNet++ model. These improvements are not simple combinations, but organic integrations and adaptive adjustments among modules based on the characteristics of forage point clouds, aiming to improve the overall preprocessing quality and efficiency. The proposed method has been validated on point cloud datasets acquired in complex pasture environments. Experimental results show that the proposed CIM method achieves an accuracy of 97.0% and a segmentation precision of 92.8%, with a processing time of only 0.009&#xa0;s, significantly improving accuracy and segmentation performance compared to other methods. This study provides a new approach for intelligent operations of forage harvesting machinery.</p>