Development and evaluation of machine learning models for predicting chicken meat production

Abstract

Chicken meat production plays a vital role in the global food industry by providing a cost-effective and sustainable protein source for a rapidly growing population. Given strategic importance, accurately estimating production levels is essential for improving operational efficiency, optimizing resource use, and responding to market demand. In recent years, data-oriented methods have become integral to modern agriculture, with machine learning models emerging as powerful tools for modeling agricultural outputs. This study aims to develop and compare predictive models for chicken meat production in Türkiye using four machine learning algorithms: Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (k-NN), and Extreme Gradient Boosting (XGBoost). The models were trained on a comprehensive dataset spanning 62 years (1961–2022), incorporating meteorological, agricultural, and economic variables. Key predictors such as feed use, weight gain, and environmental factors were included. The dataset was carefully prepared to ensure robust model training and validation. Model performance was evaluated using multiple metrics, including the coefficient of determination (R²), MAE, MSE, and RMSE. Results indicated that the Linear Regression model achieved the highest R² value and the lowest error rates among all algorithms. These findings underscore the potential of AI-based approaches to enhance decision-making and resource management in the poultry sector. With further integration of diverse data sources and advanced learning techniques, this framework can contribute to the development of more efficient, adaptive, and sustainable poultry production systems.

Keywords

• Chicken meat production
• Machine learning models
• Predictive modeling
• Agricultural data analysis
• Sustainable poultry production

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