Tree detection and in-row localization for autonomous precision orchard management

This work presents a framework for localizing ground robots within fruit tree orchards. The standard practice of managing orchards at the large block-level does not maximize the potential of farms — individual plants have different needs due to variations in soil, pests, disease, irrigation, etc. In order to make selective management decisions for individual trees, such as precision fertilization, a robot must be able to accurately localize itself within the row. This is a challenge since in high density, modern orchard systems it is often difficult to obtain accurate GNSS measurements. Our algorithm begins by using deep learning to segment a tree trunk in an RGB-D image and then estimate its width. We then use the trunk segmentations and widths to calculate particle weights in a particle filter-based localization system. We show that integrating trunk width into the particle update step led to a 45% decrease in the distance traveled before convergence, and a 31% decrease in convergence time, alongside a marginal increase in the rate of correct convergence. We also demonstrate autonomous tree-level localization with a large ground robot in realistic field experiments in a commercial apple orchard. • A Deep Learning algorithm was trained to estimate the width of tree trunks. • Trunk widths were used to calculate particle weights in a localization system. • Integrating trunk width into the particle update step led to better convergence. • Tree-level localization was demonstrated with a robot in a commercial apple orchard.