MACHINE LEARNING–DRIVEN PROCESS OPTIMIZATION
A CASE STUDY IN A POULTRY FARM IN SOUTHERN MINAS GERAIS
DOI:
https://doi.org/10.36674/mythos.v17i2.1049Keywords:
Artificial Intelligence, Egg Production, Process, Poultry Farming, Random Forest, Regression Tree M5PAbstract
This paper presents a case study on the application of Machine Learning (ML) in the optimization of egg production, conducted in the context of a poultry farm located in southern Minas Gerais, Brazil. The purpose of this research is to analyze the feasibility of optimizing the farm’s production processes using predictive ML techniques. To achieve this goal, a quantitative methodology was adopted, based on three main stages: collection and preprocessing of the farm’s historical data, development of predictive models using the WEKA software, and evaluation of the algorithms’ performance through statistical metrics such as Mean Absolute Error (MAE) and the correlation coefficient (R). Among the tested models, Random Forest showed the best performance, presenting a high correlation level and the lowest error margin, thus proving to be suitable for operational forecasting. The M5P model stood out for its balance between accuracy and interpretability, while linear regression, although simpler, delivered satisfactory and easily interpretable results. It is believed that the findings of this study may contribute to discussions and reflections on the importance of technology in the efficient management of poultry farming and its impact on small and medium-sized farms in the region, optimizing resource use and increasing sustainability.
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Copyright (c) 2025 Leonardo Romanelli Guimarães, Rodrigo Franklin Frogeri, Ana Amélia Furtado de Oliveira
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