IDiSSC: Edge-computing-based Intelligent Diagnosis Support System for Citrus Inspection

Mateus Silva, Mateus Silva, Jonathan Ferreira da Silva, Ricardo Oliveira

Abstract

Orange and citrus agriculture has a significant economic role, especially in tropical countries. The use of edge systems with machine learning techniques presents a perspective to improve the present techniques, with faster tools aiding the inspection diagnostics. The usage of cost- and resource-restrictive devices to create these solutions improves this technique’s reach capability and reproducibility. In this perspective, we propose a novel edge-computing-based intelligent diagnosis support system performing a pseudospectral analysis to improve the orange inspection processes. Our results indicate that traditional machine learning methods reach over 92% accuracy, reaching 99% on the best performance technique with Artificial Neural Networks in the binary classification stage. For multiple classes, the accuracy varies from 97% up to 98%, also reaching the best performance with Artificial Neural Networks. Finally, the Random Forest and Artificial Neural Network obtained the best results, considering algorithm parameters and embedded hardware performance. These results enforce the feasibility of the proposed application.

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Paper Citation


in Harvard Style

Silva M., Ferreira da Silva J. and Oliveira R. (2021). IDiSSC: Edge-computing-based Intelligent Diagnosis Support System for Citrus Inspection. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8, pages 685-692. DOI: 10.5220/0010444106850692


in Bibtex Style

@conference{iceis21,
author={Mateus Silva and Jonathan Ferreira da Silva and Ricardo Oliveira},
title={IDiSSC: Edge-computing-based Intelligent Diagnosis Support System for Citrus Inspection},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={685-692},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010444106850692},
isbn={978-989-758-509-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - IDiSSC: Edge-computing-based Intelligent Diagnosis Support System for Citrus Inspection
SN - 978-989-758-509-8
AU - Silva M.
AU - Ferreira da Silva J.
AU - Oliveira R.
PY - 2021
SP - 685
EP - 692
DO - 10.5220/0010444106850692