Robotic vision system design for black pepper harvesting

Robotic vision system design is developed in this paper to locate the coordinate of pepper fruits from trees and leaves, and identify pepper ripeness for harvest in Sarawak region, Malaysia. The vision system comprises of three stages, i.e. salient point localization, contour extraction and pepper verification. First, ripe peppers are spotted using visual saliency detection based on color, intensity and orientation. Three most salient regions are then determined by red component detection, whereas red element indicates a ripe pepper region. The detected red salient region is therefore shrunk to pepper edges using active contour method. To further verify the correct detection of peppers, the extracted edges are required to match with predefined shape, and to check neighborhoods similarity surrounding the region of interest. Preliminary simulation results showed that the vision system spotted the salient regions with pepper in 91.3% of success rate; contour extractions covering a pepper boundary with 84.35% of success rate and the results for pepper verification stage are promising.

[1]  Yael Edan,et al.  Robotic melon harvesting , 2000, IEEE Trans. Robotics Autom..

[2]  Giulio Reina,et al.  Agricultural robot for radicchio harvesting , 2006, J. Field Robotics.

[3]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[4]  Ying Zhang,et al.  Automatic recognition vision system guided for apple harvesting robot , 2012, Comput. Electr. Eng..

[5]  Yael Edan,et al.  Computer vision for fruit harvesting robots - state of the art and challenges ahead , 2012, Int. J. Comput. Vis. Robotics.

[6]  Tomonari Furukawa,et al.  A low-cost gripper for an apple picking robot , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[7]  Jose L Pons,et al.  A SURVEY OF COMPUTER VISION METHODS FOR LOCATING FRUIT ON TREES , 2000 .

[8]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[9]  Francisco Rodríguez,et al.  Grasping in Agriculture: State-of-the-Art and Main Characteristics , 2013 .

[10]  Tateshi Fujiura,et al.  Cherry-harvesting robot , 2008 .

[11]  Martin Mellado,et al.  Review. Technologies for robot grippers in pick and place operations for fresh fruits and vegetables , 2011 .

[12]  Naoshi Kondo,et al.  Robotics for Plant Production , 1998, Artificial Intelligence Review.

[13]  Jizhan Liu,et al.  Analysis of Workspace and Kinematics for a Tomato Harvesting Robot , 2008, 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA).

[14]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

[15]  Ali Borji,et al.  State-of-the-Art in Visual Attention Modeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  J. Bontsema,et al.  An Autonomous Robot for Harvesting Cucumbers in Greenhouses , 2002, Auton. Robots.

[17]  Kenta Shigematsu,et al.  Evaluation of a strawberry-harvesting robot in a field test , 2010 .