A novel framework for modeling dormant apple trees using single depth image for robotic pruning application

Abstract

Dormant pruning is one of the most costly and labor-intensive operations in specialty crop production. During winter, a large crew of trained seasonal workers has to carefully remove the branches from hundreds of trees using a set of pre-defined rules. The goal of automatic pruning is to reduce this dependence on a large work-force that is currently needed for the job. Automatically applying the pruning rules entails construction of 3D models of the trees in their dormant condition (that is, without foliage) and accurate estimation of the pruning points on the branches. This paper investigates the contribution of modeling the trunk and primary branches of a tree using semi-circles in a 3D reconstruction scheme. It involves three main steps; estimating the diameter-error, filtering the depth images, and semi-circle-based modeling which estimates the required pruning data. The obtained results demonstrate the effectiveness of the proposed framework for automatic reconstruction using only single depth image. We further propose a novel empirical model that estimates the diameter of the primary branches under varying depth values. Both qualitative and quantitative evaluations are performed on a new challenging apple trees dataset under various indoor and outdoor environments. Our results show that the proposed scheme provides a performance of 89% for correctly estimating the diameter of the primary branches.