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Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral †

Over the last few years, several studies have appeared that employ Artificial Intelligence (AI) techniques to improve sustainable development in the agricultural sector. Specifically, these intelligent techniques provide mechanisms and procedures to facilitate decision-making in the agri-food indust...

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Detalles Bibliográficos
Autores principales: Marco-Detchart, Cedric, Carrascosa, Carlos, Julian, Vicente, Rincon, Jaime
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007674/
https://www.ncbi.nlm.nih.gov/pubmed/36904586
http://dx.doi.org/10.3390/s23052382
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author Marco-Detchart, Cedric
Carrascosa, Carlos
Julian, Vicente
Rincon, Jaime
author_facet Marco-Detchart, Cedric
Carrascosa, Carlos
Julian, Vicente
Rincon, Jaime
author_sort Marco-Detchart, Cedric
collection PubMed
description Over the last few years, several studies have appeared that employ Artificial Intelligence (AI) techniques to improve sustainable development in the agricultural sector. Specifically, these intelligent techniques provide mechanisms and procedures to facilitate decision-making in the agri-food industry. One of the application areas has been the automatic detection of plant diseases. These techniques, mainly based on deep learning models, allow for analysing and classifying plants to determine possible diseases facilitating early detection and thus preventing the propagation of the disease. In this way, this paper proposes an Edge-AI device that incorporates the necessary hardware and software components for automatically detecting plant diseases from a set of images of a plant leaf. In this way, the main goal of this work is to design an autonomous device that allows the detection of possible diseases that can detect potential diseases in plants. This will be achieved by capturing multiple images of the leaves and implementing data fusion techniques to enhance the classification process and improve its robustness. Several tests have been carried out to determine that the use of this device significantly increases the robustness of the classification responses to possible plant diseases.
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spelling pubmed-100076742023-03-12 Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral † Marco-Detchart, Cedric Carrascosa, Carlos Julian, Vicente Rincon, Jaime Sensors (Basel) Article Over the last few years, several studies have appeared that employ Artificial Intelligence (AI) techniques to improve sustainable development in the agricultural sector. Specifically, these intelligent techniques provide mechanisms and procedures to facilitate decision-making in the agri-food industry. One of the application areas has been the automatic detection of plant diseases. These techniques, mainly based on deep learning models, allow for analysing and classifying plants to determine possible diseases facilitating early detection and thus preventing the propagation of the disease. In this way, this paper proposes an Edge-AI device that incorporates the necessary hardware and software components for automatically detecting plant diseases from a set of images of a plant leaf. In this way, the main goal of this work is to design an autonomous device that allows the detection of possible diseases that can detect potential diseases in plants. This will be achieved by capturing multiple images of the leaves and implementing data fusion techniques to enhance the classification process and improve its robustness. Several tests have been carried out to determine that the use of this device significantly increases the robustness of the classification responses to possible plant diseases. MDPI 2023-02-21 /pmc/articles/PMC10007674/ /pubmed/36904586 http://dx.doi.org/10.3390/s23052382 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Marco-Detchart, Cedric
Carrascosa, Carlos
Julian, Vicente
Rincon, Jaime
Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral †
title Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral †
title_full Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral †
title_fullStr Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral †
title_full_unstemmed Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral †
title_short Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral †
title_sort robust multi-sensor consensus plant disease detection using the choquet integral †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007674/
https://www.ncbi.nlm.nih.gov/pubmed/36904586
http://dx.doi.org/10.3390/s23052382
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