MSCVI: An improved algorithm for mitigating LiDAR noise and occlusion effects in field wheat tiller number calculation

Abstract

Tiller number is a key agronomic trait of the crop population, reflecting the adaptability of crop to the environment and the status of plant growth, as well as grain yield. As a state-of-the-art form of active remote sensing that penetrates the vegetation canopy and provides a detailed representation of 3D structures, terrestrial laser scanning (TLS) is beginning to show great potential in precisely counting the tiller number. However, current research of TLS-derived wheat canopy tiller number is commonly affected by mutual occlusion among plants and noise problem. In this study, we proposed a novel Mean Shift Clustering algorithm based on Voxel Interpolation (MSCVI) to effectively mitigate those effects by removing excess noise and interpolating unsampled voxels. The findings demonstrated that there was a strong exponential relationship between the gap fraction (Pgₐₚ) and point cloud density for wheat plots. In addition, MSCVI was effective to count the tiller number of wheat under different field treatments (R² = 0.69, RMSE = 79 tilllers/m²), producing better results than previous adaptive layering and hierarchical clustering (ALHC) algorithm. MSCVI could obtain more precise and detailed wheat canopy information by denoising and compensating the point cloud data, which greatly improved the accuracy of detecting tiller numbers under the condition of high plant density, planophile plant type and tiller stage data. This study provides new insights into effectively mitigating noise and occlusion between plants and within dense canopies, and has potential for accurate calculation of the tiller number in the assessment of crop yield phenotype.

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