{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T10:44:31Z","timestamp":1775040271804,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["PID2021-123673OB-C31"],"award-info":[{"award-number":["PID2021-123673OB-C31"]}]},{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["APOSTD\/2021\/227"],"award-info":[{"award-number":["APOSTD\/2021\/227"]}]},{"name":"European Social Fund","award":["PID2021-123673OB-C31"],"award-info":[{"award-number":["PID2021-123673OB-C31"]}]},{"name":"European Social Fund","award":["APOSTD\/2021\/227"],"award-info":[{"award-number":["APOSTD\/2021\/227"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23052382","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T02:08:34Z","timestamp":1677031714000},"page":"2382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4310-9060","authenticated-orcid":false,"given":"Cedric","family":"Marco-Detchart","sequence":"first","affiliation":[{"name":"Valencian Research Institute for Artificial Intelligence, Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3649-6530","authenticated-orcid":false,"given":"Carlos","family":"Carrascosa","sequence":"additional","affiliation":[{"name":"Valencian Research Institute for Artificial Intelligence, Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2743-6037","authenticated-orcid":false,"given":"Vicente","family":"Julian","sequence":"additional","affiliation":[{"name":"Valencian Research Institute for Artificial Intelligence, Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"},{"name":"Valencian Graduate School and Research Network of Artificial Intelligence, Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1153-0616","authenticated-orcid":false,"given":"Jaime","family":"Rincon","sequence":"additional","affiliation":[{"name":"Valencian Research Institute for Artificial Intelligence, Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A comprehensive review on automation in agriculture using artificial intelligence","volume":"2","author":"Jha","year":"2019","journal-title":"Artif. Intell. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.18034\/apjee.v6i2.542","article-title":"How Artificial Intelligence Improves Agricultural Productivity and Sustainability: A Global Thematic Analysis","volume":"6","author":"Vadlamudi","year":"2019","journal-title":"Asia Pac. J. Energy Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Benos, L., Tagarakis, A.C., Dolias, G., Berruto, R., Kateris, D., and Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21.","DOI":"10.3390\/s21113758"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s41348-020-00368-0","article-title":"Plant disease detection using computational intelligence and image processing","volume":"128","author":"Vishnoi","year":"2021","journal-title":"J. Plant Dis. Prot."},{"key":"ref_5","first-page":"229","article-title":"A review of imaging techniques for plant disease detection","volume":"4","author":"Singh","year":"2020","journal-title":"Artif. Intell. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"56683","DOI":"10.1109\/ACCESS.2021.3069646","article-title":"Plant disease detection and classification by deep learning\u2014A review","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_7","first-page":"354","article-title":"A review of neural networks in plant disease detection using hyperspectral data","volume":"5","author":"Golhani","year":"2018","journal-title":"Inf. Process. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Orchi, H., Sadik, M., and Khaldoun, M. (2022). On using artificial intelligence and the internet of things for crop disease detection: A contemporary survey. Agriculture, 12.","DOI":"10.3390\/agriculture12010009"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21219","DOI":"10.1109\/ACCESS.2022.3152544","article-title":"IoT-equipped and AI-enabled next generation smart agriculture: A critical review, current challenges and future trends","volume":"10","author":"Qazi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Marco-Detchart, C., Rincon, J., Julian, V., and Carrascosa, C. (2022). Plant Disease Detection: An Edge-AI Proposal. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection: International Workshops of PAAMS 2022, L\u2019Aquila, Italy, 13\u201315 July 2022, Springer.","DOI":"10.1007\/978-3-031-18697-4_9"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105334","DOI":"10.1016\/j.compag.2020.105334","article-title":"A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms","volume":"172","author":"Costa","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.biosystemseng.2016.01.017","article-title":"A review on the main challenges in automatic plant disease identification based on visible range images","volume":"144","author":"Barbedo","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/34.56205","article-title":"Scale-space and edge detection using anisotropic diffusion","volume":"12","author":"Perona","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","unstructured":"Marco-Detchart, C., Lopez-Molina, C., Fernandez, J., and Bustince, H. (2017). Advances in Fuzzy Logic and Technology 2017, Springer."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.fss.2021.02.022","article-title":"Non-linear scale-space based on fuzzy contrast enhancement: Theoretical results","volume":"421","author":"Madrid","year":"2021","journal-title":"Fuzzy Sets Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.compag.2009.01.003","article-title":"Image pattern classification for the identification of disease causing agents in plants","volume":"66","author":"Camargo","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.compag.2010.06.009","article-title":"Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance","volume":"74","author":"Rumpf","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","unstructured":"Gueye, Y., and Mbaye, M. (2020). International Conference on Service-Oriented Computing, Springer."},{"key":"ref_19","first-page":"3506","article-title":"Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment","volume":"34","author":"Khan","year":"2022","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_20","unstructured":"Villaret, M., Alsinet, T., Fern\u00e1ndez, C., and Valls, A. (2021). Frontiers in Artificial Intelligence and Applications, IOS Press."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106523","DOI":"10.1016\/j.compag.2021.106523","article-title":"Towards automatic field plant disease recognition","volume":"191","author":"Gui","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101182","DOI":"10.1016\/j.ecoinf.2020.101182","article-title":"Plant leaf disease classification using EfficientNet deep learning model","volume":"61","author":"Atila","year":"2021","journal-title":"Ecol. Informatics"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_25","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (\u2013, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (\u2013, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_28","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_29","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017). Dynamic routing between capsules. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e752","DOI":"10.7717\/peerj-cs.752","article-title":"CapPlant: A capsule network based framework for plant disease classification","volume":"7","author":"Samin","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5390","DOI":"10.1109\/ACCESS.2022.3141371","article-title":"Plant disease identification using a novel convolutional neural network","volume":"10","author":"Hassan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_32","first-page":"1","article-title":"A novel plant disease prediction model based on thermal images using modified deep convolutional neural network","volume":"24","author":"Bhakta","year":"2022","journal-title":"Precision Agriculture"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kristiani, E., Yang, C.T., and Nguyen, K.L.P. (2020, January 3\u20135). Optimization of deep learning inference on edge devices. Proceedings of the 2020 International Conference on Pervasive Artificial Intelligence (ICPAI), Taipei, Taiwan.","DOI":"10.1109\/ICPAI51961.2020.00056"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Beliakov, G., Bustince Sola, H., and Calvo, T. (2016). A Practical Guide to Averaging Functions, Springer International Publishing.","DOI":"10.1007\/978-3-319-24753-3"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TFUZZ.2015.2453020","article-title":"Preaggregation Functions: Construction and an Application","volume":"24","author":"Lucca","year":"2016","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ins.2017.12.029","article-title":"CF-integrals: A new family of pre-aggregation functions with application to fuzzy rule-based classification systems","volume":"435","author":"Lucca","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_38","unstructured":"Hughes, D., and Salath\u00e9, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201323). Learning transferable architectures for scalable Image recognition. Proceedings of the IEEE Conference on Computer vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:38:23Z","timestamp":1760121503000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23052382"],"URL":"https:\/\/doi.org\/10.3390\/s23052382","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,21]]}}}