Measuring and modeling apple trees using time-of-flight data for automation of dormant pruning applications

Abstract

Dormant pruning is one of the most expensive, labor-intensive, but, unavoidable procedure in the field of horticulture to ensure quality crop production. During winter, skilled farmers remove certain branches that are connected directly with the trunk of a tree carefully using a set of predefined rules. In order to reduce this dependence on a large manpower, our goal is to automate this pruning process by building 3D models of dormant apple trees, which eventually would be fed to an intelligent robotic system. In this paper, we present a semicircle fitting based robust 3D reconstruction scheme for modeling the trunk and primary branches of apple trees. The method involves estimating the diameter-error, creating semicircle fit model of the tree from a single depth image, and reconstructing the final 3D model of the tree by aligning a sequence of depth images. Analysis of the qualitative as well as the quantitative evaluations of our algorithm on five different dormant apple trees from our dataset under various indoor and outdoor environments demonstrate the effectiveness of the proposed framework for automatic 3D reconstruction. The results show that on an average, the proposed schemes provide a performance of 89.4% for correctly estimating the diameters of the primary branches with a tolerance of 5 mm and 100%c for correctly identifying the branches.