Click on “Download PDF” for the PDF version or on the title for the HTML version.


If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Tree Canopy Estimation for Mechanical Pruning based on 3D LIDAR

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

Citation:  Paper number  18-054,  2018 Section Meeting Papers. (doi: 10.13031/nabec.2018-054) @2018
Authors:   Juan Feng, Long He
Keywords:   Mechanical pruning, 3D LIDAR, Tall spindle tree, Canopy estimation

Abstract. Pruning is a labor- and time-demanding operation for apple production. Generally, it is the second greatest annual in-field expense for tree fruit growers just behind harvesting. As more and more growers are adopting planar orchard systems, which improves the potential of mechanization for orchard operations, including pruning. In typical mechanical pruning operation, a pruning mechanism is mounted on a tractor operated by a tractor driver. The driver typically drives in the middle of path straightly as possible. However, the shape of canopy normally varies along the tree rows. Therefore, in order to keep pruning consistent, it is essential to adjust the cutter according to the tree canopy shape during pruning operation. A 3D LIDAR based scanning system is developed in this study to estimate the shape of the canopy. Point cloud data was acquired from ten ‘Tall spindle’ apple trees, and then processed in MATLAB environment. Firstly, tree trunk was extracted as reference axis by a novel algorithm based on a cylinder accumulator, and then the canopy edge points were detected by the convex hull principle. Lastly, discretization degree of edge points were obtained by calculating the projection distance between edge points and reference axis. The results indicated that main trunk extraction was accurate, and the discretion degree of the key points of the canopy could reflect the changing trend of the outer surface of the canopy and was helpful to locate the abnormal branches. The outcome from this study will provide baseline information for automated cutter position adjustment in mechanical tree pruning.

(Download PDF)    (Export to EndNotes)