Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting to Environmental Control
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 467
Special Issue Editors
Interests: sustainable animal production; smart farming; environmental and animal variables modeling and control; agriculture monitoring; animal behavior; agriculture emissions
Special Issues, Collections and Topics in MDPI journals
Interests: animal farming; environmental and animal variables modeling and control; animal welfare; machine learning; clean air farming
Special Issue Information
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- precision livestock farming
- artificial intelligence
- algorithm
- sensors
- animal housing
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