Convolutional neural networks for the Hass avocado classification using LabVIEW in an agro-industrial plant

Cruz De La Cruz J.E.; Vera Ramirez O.J. · Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 (2020) · DOI: 10.1109/intercon50315.2020.9220246

Resumen

Peru is currently the world's third-largest exporter of Hass avocados according to the latest statistics from FAOSTAT. To classify avocados efficiently in size and maturity, a robust artificial intelligence plant was implemented to classify avocados into 5 categories. This grading technique differs from traditional grading in that it is non-invasive, reducing avocado damage by manually inspecting and grading. The plant comprises the step of hardware, consisting of Aca2500 Basler camera, lens HR 2mm/F1, illuminated, and the conveyor belt 1200. The step s7 PLC software: TIA PORTAL (OPC), a sequential algorithm, and convolutional neural network decision in which the selection parameters size and color of avocado include. The classification process fulfills three main stages: Image acquisition, processing, and recognition. Convolutional neural networks were used for image treatment, obtaining an average classification precision of 60% in real-time. From the results obtained, we see that the classification can be improved.

Instituciones: Universidad Nacional del Altiplano; Universidad Nacional de Moquegua