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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/5/2382 |
| _version_ | 1797614356383399936 |
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| author | Cedric Marco-Detchart Carlos Carrascosa Vicente Julian Jaime Rincon |
| author_facet | Cedric Marco-Detchart Carlos Carrascosa Vicente Julian Jaime Rincon |
| author_sort | Cedric Marco-Detchart |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T07:11:17Z |
| format | Article |
| id | doaj.art-4856e5e61c654f28bf902c0791d3d578 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:11:17Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4856e5e61c654f28bf902c0791d3d5782023-11-17T08:33:58ZengMDPI AGSensors1424-82202023-02-01235238210.3390/s23052382Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet IntegralCedric Marco-Detchart0Carlos Carrascosa1Vicente Julian2Jaime Rincon3Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, SpainValencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, SpainValencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, SpainValencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, SpainOver 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.https://www.mdpi.com/1424-8220/23/5/2382smart agriculturemachine learningEDGE-AIsensors |
| spellingShingle | Cedric Marco-Detchart Carlos Carrascosa Vicente Julian Jaime Rincon Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral Sensors smart agriculture machine learning EDGE-AI sensors |
| 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 | smart agriculture machine learning EDGE-AI sensors |
| url | https://www.mdpi.com/1424-8220/23/5/2382 |
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