Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis
| Autor: | Antonio Ruiz Canales, Meritxell Justicia Segovia, José Miguel Madueño Luna, Antonio Madueño Luna, Alberto Lucas Pascual, Manuel de Jódar Lázaro, José Miguel Molina Martínez |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos, Universidad de Sevilla. AGR280: Ingeniería Rural |
| Rok vydání: | 2020 |
| Předmět: |
Teensy
Computer science Artificial neural networks (ANNs) table olive pitting CM1K chip lcsh:Chemical technology 01 natural sciences Biochemistry Slicing Article Intel Curie chip Analytical Chemistry Internet of things (IoT) lcsh:TP1-1185 Computer vision Electrical and Electronic Engineering slicing and stuffing machines Instrumentation Table olive pitting artificial neural networks (ANNs) Artificial neural network business.industry 010401 analytical chemistry Process (computing) 04 agricultural and veterinary sciences Atomic and Molecular Physics and Optics 0104 chemical sciences Slicing and stuffing machines 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Artificial intelligence business |
| Zdroj: | Sensors Volume 20 Issue 5 idUS: Depósito de Investigación de la Universidad de Sevilla Universidad de Sevilla (US) Sensors, Vol 20, Iss 5, p 1541 (2020) idUS. Depósito de Investigación de la Universidad de Sevilla instname Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s20051541 |
| Popis: | 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&ndash 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 &ldquo boat&rdquo ), 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. |
| Databáze: | OpenAIRE |
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