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Convolutional Neural Network and Industrial Robot Arm applied to an automatic coffee bean selection system

Abstract:

The objective of this paper is to design and implement an automatic coffee bean selection system, based on the integration of a Scara Epson robot arm with a Convolutional Neural Network - based classifier. The implemented system extracts the elements that were identified as coffee beans with shape and color alterations. The hardware architecture consists of: Epson Scara LS10 robot arm, RC-90B controller, 4-megapixel webcam, an extraction end effector, and white illumination. The software architecture consists of: image acquisition, segmentation and preprocessing algorithms, training and classification algorithms with Convolutional Neural Networks (224/2 input/output layers), and robot arm motion control algorithms. For the performance evaluation of the automatic classification algorithms, 18 tests were performed considering 3 different cases of separation between grains, greater than 5 mm, 3 to 4 mm, and less than 2 mm. As a result, an effectiveness percentage of 100% was obtained for the first and second range of separation, and a percentage of 61.5% for the third range, due to the overlapping between coffee beans.