Title
Convolutional neural networks for the Hass avocado classification using LabVIEW in an agro-industrial plant
Date Issued
01 September 2020
Access level
metadata only access
Resource Type
conference paper
Author(s)
Vera Ramirez O.J.
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
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.
Language
English
OCDE Knowledge area
Ingeniería eléctrica, Ingeniería electrónica
Subjects
Scopus EID
2-s2.0-85095408751
ISBN of the container
9781728193779
Conference
27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
Sources of information:
Directorio de Producción Científica
Scopus