Sistema de clasificación automática de café basado en un brazo robótico industrial y máquina de visión

Abstract

Abstract: The objective of the present project is to design and implement an automatic coffee bean selection system, based on the integration of a Scara Epson robot arm in conjunction with an automatic classifier based on a Convolutional Neural Network. 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, extraction end effector, and 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, a percentage of effectiveness of 100% was obtained for the first two cases of separation, and a percentage of 61.5% for the third case, due to the overlap between coffee beans.