Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting to Environmental Control

Dear Colleagues,

The integration of machine learning (ML) into precision livestock farming (PLF) represents a significant leap forward in managing livestock populations more efficiently and sustainably. ML technologies are revolutionizing the way farmers monitor, manage, and predict animal behavior and health, providing valuable insights from animal housing to the prediction of activity patterns or more efficient ways of environmental control. As the industry faces increasing challenges, including environmental changes, ML offers innovative solutions to gain deeper insights into individual animals' health and welfare, such as detecting early signs of disease, assessing stress levels, predicting changes in feeding and movement patterns, or the evolution of indoor air quality.

ML in precision livestock farming (PLF) helps to predict animal behavior by analyzing past data, allowing farmers to prevent issues like overcrowding or food shortages. It also forecasts disease risks, enabling early treatment. Additionally, ML optimizes barn conditions (temperature, airflow) to reduce animal stress and adjusts feeding schedules for better health and productivity.

This Special Issue aims to explore the latest advancements in ML applications in precision livestock farming, from animal housing management to activity forecasting and predictive health monitoring. We invite contributions that examine the intersection of data analytics, sensor technologies, and AI models, as well as studies focusing on the sustainability and ethical considerations of implementing these technologies in livestock farming. By fostering interdisciplinary collaboration, this Special Issue aims to contribute to the development of more efficient, ethical, and sustainable farming practices.

Dr. María Dolores Fernández Rodríguez
Dr. Roberto Besteiro
Guest Editors

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