Palavras-chave
convolutional neural network
deep learning
peaches
rust fungus
Peach leaf curl fungus aprendizaje profundo
durazno
redes neuronales convolucionales
roya
torque Roya
Torque
Redes Neuronais Convolucionais
Pêssego
Aprendizagem Profunda
deep learning
peaches
rust fungus
Peach leaf curl fungus aprendizaje profundo
durazno
redes neuronales convolucionales
roya
torque Roya
Torque
Redes Neuronais Convolucionais
Pêssego
Aprendizagem Profunda
Como Citar
Lara Rodríguez, L. D., López Meléndez, E., & Castellanos Corzo, A. L. (2023). Comparação do RNC para a detecção de pragas na safra de pêssego no Departamento de Norte de Santander cultivo de pêssego no Departamento de Norte de Santander, Colômbia. Informador Técnico, 87(2), 150–164. https://doi.org/10.23850/22565035.5805
Resumo
Devido à necessidade de aumentar o rendimento das colheitas, para favorecer o meio ambiente, produtos de proteção de culturas têm sido usados para evitar o aparecimento de pragas e doenças que geram perdas, ou complicações de natureza quaternária que impactam muito mais na comercialização e produção na agricultura, o que causou a geração de ferramentas tecnológicas para a detecção de forma preventiva; para o manejo de diferentes pragas e doenças de culturas agrícolas. O manuscrito apresenta o uso de técnicas de aprendizado de máquina, como redes neurais convolucionais e aumento de dados. O manuscrito apresenta o uso de técnicas de aprendizado de máquina, como redes neurais convolucionais e aumento de dados; alguns modelos de arquitetura sequencial com aumento de dados são propostos e comparados com modelos de arquitetura sequencial com aumento de dados. A partir de uma arquitetura sequencial com aumento de dados, são propostos e comparados dois modelos de arquitetura sequencial com aumento de dados que contribuem para a detecção correta de Torque e Ferrugem, que são as principais as principais afetações no declínio da produção de pêssego na área norte de Santander - Colômbia. Santander - Colômbia.
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