UM MODELO BASEADO EM TRANSFERÊNCIA DE APRENDIZADO PROFUNDO PARA DETECÇÃO DO MAL-DO-PANAMÁ
DOI:
https://doi.org/10.56238/revgeov16n4-073Palavras-chave:
Aprendizado Profundo, Detecção, Mal-do-panamáResumo
Abordagens de aprendizado profundo são aplicáveis ao processo produtivo da banana (Musa spp.). A fitopatologia mal-do-Panamá pode causar perdas de até 100% na cultura. Como mecanismo de detecção, técnicas de extração de características em imagens podem ser aplicadas pela Transferência de Aprendizado Profundo (DTL). Este trabalho visa aplicar e avaliar um algoritmo DTL capaz de classificar tal doença a partir de 102 imagens de folhas da bananeira doentes e 264 sadias. Foram testados seis modelos neurais pré-treinados do estado da arte e avaliados com base em: acurácia (ACC), f1-score (F1), área sob a curva (AUC) e erro médio quadrático (MSE). Todos obtiveram acurácia acima de 98%. A detecção ágil deve auxiliar o bananicultor à tomada de decisão hábil.
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