Accessible drone image processing for sustainable resource management of 3D tree-like crops using unsupervised algorithms

This study presents a novel AI-driven solution designed to enhance sustainable resource management and increase accessibility for farmers through visualized and digitalized tree information. The proposed methodology includes optimizing drone mapping for effective data coverage, capturing high-resolution imagery, and generating a 3D Point Cloud, with a particular emphasis on the integration of Weighted K-means, Density-based spatial clustering of applications with noise (DBSCAN), and cluster separation algorithms to develop a high-precision, training-free stepwise crop segmentation algorithm for extracting essential phenotypic traits such as crop height and canopy volume. The algorithm was validated using avocado trees in Tanzania and hazelnut trees in Canada, demonstrating its robustness across different tree types and planting conditions. Notably, this study highlights the feasibility of extracting crop height without constructing a Digital Terrain Model (DTM), thereby mitigating errors associated with DTM inaccuracies. The proposed stepwise segmentation algorithm achieved a height estimation R2 of 0.967 and volume estimation R2 of 0.91 for avocado trees, demonstrating high accuracy without the need for DTM or training data. The developed algorithm, coupled with a user-friendly graphical user interface (GUI), provides farmers with a reliable tool for monitoring crop health and optimizing field management, representing a significant advancement in precision agriculture technology with wide-ranging applicability.