The Analysis of Apple Orchard from Three-dimensional Point Cloud Data for Precision Agriculture

Abstract

This article discusses the evolution of precision agriculture aimed at optimizing resource allocation and management through the detailed analysis of field data. Specifically, the study focuses on the use of 3D point cloud analysis to capture the spatial distribution of features within apple orchards, establishing itself as a powerful tool for such applications. The research analyzed a dataset from apple orchards collected in 2020. By examining these data points, we sought to derive critical insights, including individual tree segmentation and canopy density mapping. The work unfolds in two main phases. The first phase involves creating a point cloud map that shows the location of every branch and leaf. The second phase focuses on developing a digital elevation model that provides a detailed representation of the orchard's ground level. This model is essential for understanding land topography and water flow patterns. The research underscores the significance of laser technology and digital mapping in enhancing the precision of agricultural practices.