Detecção de doenças de folhas de plantas usando o progresso recente em técnicas de identificação baseadas em aprendizado profundo
DOI:
https://doi.org/10.5935/jetia.v7i30.768Resumo
Principalmente a economia depende profundamente da eficiência da agricultura. As lavouras são comumente afetadas pela doença. Uma vez que a economia depende da agricultura, esta é uma das principais razões pelas quais a identificação de infecções em plantas assume uma função significativa no campo da horticultura. Na chance de que uma consideração legítima não seja tomada aqui, nesse ponto, isso causa consequências naturais para as plantas e por causa das quais a qualidade, quantidade ou eficiência de um determinado item são influenciadas. O infortúnio da colheita devido a doenças influencia consideravelmente a economia e prejudica o acesso aos alimentos. A localização rápida e precisa de doenças da planta é essencial para expandir a eficiência da agricultura de uma maneira sustentável. Em qualquer caso, a localização da planta por especialistas humanos é cara, tediosa e às vezes irreal. Para combater essas dificuldades, os fitopatologistas querem uma estrutura de conclusão exata e confiável de doenças de plantas. A utilização contínua do procedimento de aprendizado profundo com métodos de processamento de imagem para reconhecimento de doenças de plantas tornou-se um assunto de exame quente para fornecer análises programadas. Esta pesquisa fornece uma técnica de prova distintiva de doença de planta produtiva dependente de modelos de aprendizagem profunda pré-preparados, como os designs AlexNet e GoogleNet. Acreditamos que este trabalho será um recurso significativo para analistas da área de reconhecimento de doenças, utilizando estratégias de manuseio de imagens com arquiteturas de aprendizado profundo.
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