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3D Reconstruction of Apple Trees for Mechanical Pruning

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2011 Louisville, Kentucky, August 7-10, 2011  1111613.(doi:10.13031/2013.38139)
Authors:   Bikram Adhikari, Manoj Karkee
Keywords:   3D machine vision, selective pruning, skeletonization, central leader architecture, point cloud, agricultural automation

Pruning is a labor intensive operation in fruit production, which constitutes a significant component of total production cost. Automation and mechanization can reduce labor demand from such labor intensive tasks. This work focused on development of a three dimensional (3D) machine vision system to map apple trees for automatic pruning. A sensor platform consisting of a time-of-flight-of-light-based 3D camera and a color vision camera was developed to move the sensors along a row of apple trees. A set of images were collected in a young commercial orchard nearby Washington State University Irrigated Agriculture Research and Extension Center (IAREC), Prosser, WA. The trees were trained in the central leader-based fruiting wall architecture. These 3D images were processed to remove noise and plants that did not belong to the row adjacent to the sensors. A 3D medial axis thinning-based skeletonization algorithm was used to obtain the 3D structures of these trees. An algorithm to identify trunk and branches was introduced. Pruning points were located in the reconstructed tree using a simplified two step pruning rule; remove branches longer than certain value and maintain certain spacing between branches. The threshold values for the length and the spacing were provided as input parameters to the system. Based on the analysis of a reconstructed model of an apple tree, the algorithm achieved about 90% accuracy in identifying trunks, branches and pruning points. Further experiments are needed for physical calibration of 3D skeleton and to develop statistical performance measures of the 3D reconstruction and pruning point identification methods.

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