Reconstruction and spatial distribution analysis of maize seedlings based on multiple clustering of point clouds

Abstract

This study proposes a method for maize seedling reconstruction and spatial distribution analysis based on ground-based laser three-dimensional point cloud scanning technology. Using high-precision terrestrial laser scanning (TLS), 3D point cloud data was collected from multiple maize seedling plots, followed by detailed preprocessing and analysis using Trimble Realworks. During the data processing, a regression-based empirical formula, grounded in maize seedling growth characteristics, was proposed. This formula effectively mitigates the challenges of leaf occlusion in densely planted conditions, providing a solution for further point cloud segmentation and analysis. In terms of algorithm design, this study combines DBSCAN and K-means clustering algorithms to effectively overcome the challenges posed by the dense distribution of plants, leaf occlusion, and noise in the point cloud data. Through this multi-clustering approach, plant positions and distributions were accurately identified, row and column spacing calculations were optimized, and a missing plant detection function was implemented. Furthermore, a dynamic plant height calculation method based on ground undulation was proposed, significantly improving the accuracy of plant height measurement and addressing errors caused by terrain variations. Experimental results show that the proposed algorithm achieves high accuracy and robustness across multiple experimental plots, with a plant counting accuracy rate of 98.33%, a row and column spacing deviation rate controlled within 5%, and a plant height calculation accuracy exceeding 97%. These results demonstrate the effectiveness of this method in precise measurement and spatial distribution analysis during the maize seedling stage, providing strong support for precision agriculture. In the future, with further optimization of the technology, this method could be widely applied in agricultural automation and intelligent management.