Abstract
Over the last few years, there have been many technological approaches whose application domain is rural areas to provide more advanced services to environments of this kind. In the agricultural environment, several proposals mainly try to develop crop management systems based on the spatial and temporal variability of different factors within a crop field, which is currently known as precision agriculture. One of the most critical tasks in this area is to detect plant diseases. Identifying diseases requires a lot of time and skilled labour. Thus, this paper proposes developing an intelligent device to detect plant diseases using deep learning techniques. Different experiments have been carried out to evaluate the feasibility of the proposed device. The results have shown a high performance with very short execution times.
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Acknowledgements
This work was partially supported by the Spanish Government with grant numer PID2021-123673OB-C31, Universitat Politecnica de Valencia Research Grant PAID-10-19 and Consellería d’Innovació, Universitats, Ciencia i Societat Digital from Comunitat Valenciana (APOSTD/2021/227) through the European Social Fund (Investing In Your Future).
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Marco-Detchart, C., Rincon, J.A., Julian, V., Carrascosa, C. (2022). Plant Disease Detection: An Edge-AI Proposal. In: González-Briones, A., et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection. PAAMS 2022. Communications in Computer and Information Science, vol 1678. Springer, Cham. https://doi.org/10.1007/978-3-031-18697-4_9
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DOI: https://doi.org/10.1007/978-3-031-18697-4_9
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