Identification of pruning branches in tall spindle apple trees for automated pruning

Pruning is a labor intensive operation that constitutes a significant component of total apple production cost. As growers are adapting simpler, narrower, more accessible and productive (SNAP) tree architectures such as the tall spindle fruiting wall system, new opportunities have emerged to reduce pruning cost and labor through automated pruning. This work focused on identification of pruning branches on apple trees in a tall spindle architecture. A time-of-flight-of-light-based three dimensional (ToF 3D) camera was used to construct 3D skeletons of apple trees. Pruning branches were identified in the reconstructed trees using a simplified two-step pruning rule; (i) maintain specified branch spacing and (ii) maintain specified branch length. Performance of the algorithm was optimized using a training sample of 10 trees to achieve human worker’s pruning level. With a selected branch spacing (28 cm) and branch length (20 cm), the algorithm achieved 19.5% branch removal with the training dataset and 19.8% of branch removal with the validation dataset (10 trees) compared to 22% average branch removal by workers. Root Mean Square Deviation (RMSD) between human and algorithm in number of branches identified for pruning was 10% for the training dataset and 13% for the validation dataset. The algorithm and the human pruning resulted in similar average branch spacing. The algorithm maintained an average spacing of 35.7 cm for validation set whereas the average spacing for three workers was 33.7 cm. RMSD in branch spacing between the algorithm and the workers was found to be 13%. The algorithm removed 85% of long branches whereas the overlapping branch removal was only 69%. With some additional work to improve the performance in terms of overlapping branch removal, it is expected that this work will provide a good foundation for automated pruning of tall spindle apple trees in the future.

[1]  G. E. Rehkugler,et al.  Grapevine Cordon Following Using Digital Image Processing , 1989 .

[2]  K. Hartmann,et al.  Data-Fusion of PMD-Based Distance-Information and High-Resolution RGB-Images , 2007, 2007 International Symposium on Signals, Circuits and Systems.

[3]  Tien-Fu Lu,et al.  Image Processing and Analysis for Autonomous Grapevine Pruning , 2006, 2006 International Conference on Mechatronics and Automation.

[4]  Amy Tabb.  Three-dimensional Reconstruction of Fruit Trees by a Shape from Silhouette Method , 2009 .

[5]  Lie Tang,et al.  Automatic inter-plant spacing sensing at early growth stages using a 3D vision sensor , 2012 .

[6]  B. Velázquez Martí,et al.  The Influence of Mechanical Pruning in Cost Reduction, Production of Fruit, and Biomass Waste in Citrus Orchards , 2010 .

[7]  R. D. Tillett,et al.  Image Analysis for Pruning of Long Wood Grape Vines , 1997 .

[8]  Robert H. Beede,et al.  Effects of mechanical pruning on grapes , 1980 .

[9]  Reinhard Koch,et al.  A Comparison of PMD-Cameras and Stereo-Vision for the Task of Surface Reconstruction using Patchlets , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Qi Wang,et al.  Three-Dimensional Reconstruction of a Dormant Tree Using RGB-D Cameras , 2013 .

[11]  Bikram Adhikari,et al.  3D Reconstruction of Apple Trees for Mechanical Pruning , 2011 .

[12]  Terence L. Robinson,et al.  The Evolution Towards More Competitive Apple Orchard Systems in the USA , 2008 .

[13]  Rangasami L. Kashyap,et al.  Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms , 1994, CVGIP Graph. Model. Image Process..

[14]  Loren Gautz,et al.  Effect of Mechanized Pruning on Coffee Regrowth and Fruit Maturity Timing , 2002 .