Coffee Plant Leaf Disease Detection for Digital Agriculture




Deep Learning, Digital Agriculture, Computer Vision, Artificial Neural Networks, Coffee


In an effort to advance Digital Agriculture, this paper provides a comparative assessment of Artificial Neural Networks for intelligent detection of a major biotic stress factors in coffee cultivation. Through a multi-class Computer Vision task, the superior performance of Convolutional Neural Networks, notably the ShuffleNet architecture, was discerned, further substantiated by statistical analyses. This model's performance, akin to state-of-the-art solutions, was achieved with reduced training data and parameter requirements. Robustness was affirmed through external validation using alternative datasets. This contribution directly enhances coffee plantations' quality and supports the development of Edge Computing devices for Agricultural IoT.


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How to Cite

ALBUQUERQUE, L. D.; GUEDES, E. B. Coffee Plant Leaf Disease Detection for Digital Agriculture. Journal on Interactive Systems, Porto Alegre, RS, v. 15, n. 1, p. 220–233, 2024. DOI: 10.5753/jis.2024.3804. Disponível em: Acesso em: 24 apr. 2024.



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