Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

Alberto Lucas Pascual; Antonio Madueño Luna; Manuel de Jódar Lázaro; José Miguel Molina Martínez; Antonio Ruiz Canales; José Miguel Madueño Luna; Meritxell Justicia Segovia

doi:10.3390/s20051541

journal article Open Access Mar 10, 2020

Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

Alberto Lucas Pascual Antonio Madueño Luna

Manuel de Jódar Lázaro

José Miguel Molina Martínez

Antonio Ruiz Canales José Miguel Madueño Luna

Meritxell Justicia Segovia

Sensors Vol. 20 No. 5 pp. 1541 · MDPI AG

View at Publisher Save 10.3390/s20051541

Abstract

Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a “boat”), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used.

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Details

Published: Mar 10, 2020
Vol/Issue: 20(5)
Pages: 1541
License: View

Authors

A

Alberto Lucas Pascual

Food Engineering Department, Technical University of Cartagena, 30203 Cartagena, Spain

A

Antonio Madueño Luna

Aerospace Engineering and Fluid Mechanical Department, University of Seville, 41013 Seville, Spain

M

Manuel de Jódar Lázaro

Food Engineering Department, Technical University of Cartagena, 30203 Cartagena, Spain

J

José Miguel Molina Martínez

Food Engineering Department, Technical University of Cartagena, 30203 Cartagena, Spain

A

Antonio Ruiz Canales

Engineering Department, Miguel Hernández University of Elche, 03312 Orihuela, Spain

J

José Miguel Madueño Luna

Graphics Engineering Department, University of Seville, 41013 Seville, Spain

M

Meritxell Justicia Segovia

Engineering Department, Miguel Hernández University of Elche, 03312 Orihuela, Spain

Cite This Article

Alberto Lucas Pascual, Antonio Madueño Luna, Manuel de Jódar Lázaro, et al. (2020). Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis. Sensors, 20(5), 1541. https://doi.org/10.3390/s20051541

Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

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