Registration of Three‐Dimensional Point Clouds in Agricultural Environments by Deep Learning‐Based Architecture Utilizing Correspondence Between Point Clouds

Point cloud registration within agricultural environments presents unique challenges due to cluttered scenes, repetitive plant structures, partial overlaps and temporal variability. Traditional methods relying on hand‐crafted features often struggle with the complexity and irregularity of such datasets, leading to limited performance. In this study, a registration methodology is proposed that utilizes the PointNet++ architecture, designed to automatically learn features from point cloud data and overcome these limitations. The methodology involves three main steps: (1) sampling and canonicalization to preprocess local patches of point clouds, (2) designing a deep learning architecture to extract robust and distinctive features, and (3) registering point clouds using learned descriptors. Experimental validation was conducted using the ETH dataset and two agricultural datasets collected from test sites with cabbage and maize crops. The proposed method achieved a root mean square error (RMSE) of 4.21 cm in a cabbage field and RMSE values of 5.48 cm, 3.45 cm, and 2.06 cm at the seedling, jointing, and flowering growth stages of maize crops, respectively. Additionally, the method outperformed classical and recent techniques in terms of feature matching recall (FMR), demonstrating its reliability, accuracy, and potential for industrial applications. These results underscore the effectiveness of the proposed deep learning‐based approach in handling the dynamic and nonrigid nature of agricultural environments.