Generating Multispectral Point Clouds for Digital Agriculture

Abstract

Digital agriculture is increasingly important for plant-level analysis, enabling detailed assessments of growth, nutrition and overall condition. Multispectral point clouds are promising due to the integration of geometric and radiometric information. Although RGB point clouds can be generated with commercial terrestrial scanners, multi-band multispectral point clouds are rarely obtained directly. Most existing methods are limited to aerial platforms, restricting close-range monitoring and plant-level studies. Efficient workflows for generating multispectral point clouds from terrestrial sensors, while ensuring geometric accuracy and computational efficiency, are still lacking. Here, we propose a workflow combining photogrammetric and computer vision techniques to generate high-resolution multispectral point clouds by integrating terrestrial light detection and ranging (LiDAR) and multispectral imagery. Bundle adjustment estimates the camera’s position and orientation relative to the LiDAR reference system. A frustum-based culling algorithm reduces the computational cost by selecting only relevant points, and an occlusion removal algorithm assigns spectral attributes only to visible points. The results showed that colourisation is effective when bundle adjustment uses an adequate number of well-distributed ground control points. The generated multispectral point clouds achieved high geometric consistency between overlapping views, with displacements varying from 0 to 9 mm, demonstrating stable alignment across perspectives. Despite some limitations due to wind during acquisition, the workflow enables the generation of high-resolution multispectral point clouds of vegetation.