OTIMIZAÇÃO DE PROCESSOS ORIENTADO POR MACHINE LEARNING
estudo de caso em uma granja sul mineira
DOI:
https://doi.org/10.36674/mythos.v17i2.1049Palavras-chave:
Inteligência Artificial, Produção de Ovos, Processo, Avicultura, Árvore de Regressão M5P, Floresta AleatóriaResumo
O presente trabalho apresenta um estudo de caso sobre a aplicação de Machine Learning (ML) na otimização da produção de ovos, vivenciado no contexto de uma granja sul mineira. Através desta pesquisa, busca-se analisar a viabilidade de otimizar os processos produtivos da granja utilizando técnicas preditivas de ML. Para isso, adotou-se uma metodologia quantitativa baseada em três etapas principais: coleta e tratamento de dados históricos da granja, construção de modelos preditivos utilizando o software WEKA, e avaliação do desempenho dos algoritmos aplicados por meio de métricas estatísticas como erro médio absoluto (MAE) e coeficiente de correlação (R). Entre os modelos testados, o Random Forest apresentou o melhor desempenho, com elevado grau de correlação e menor margem de erro, mostrando-se ideal para previsões operacionais. O M5P destacou-se pelo equilíbrio entre precisão e interpretabilidade, enquanto a regressão linear, embora mais simples, ofereceu resultados satisfatórios e de fácil compreensão. Acredita-se que os resultados deste relato possam contribuir para o debate e reflexões sobre a importância da tecnologia na gestão eficiente da avicultura e seu impacto em pequenas e médias granjas da região, otimizando o uso de recursos e aumentando a sustentabilidade.
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Copyright (c) 2025 Leonardo Romanelli Guimarães, Rodrigo Franklin Frogeri, Ana Amélia Furtado de Oliveira
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