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Agriculture
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Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting...
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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: 20 May 2026 | Viewed by 5356

Special Issue Editors

Dr. María Dolores Fernández Rodríguez

E-Mail Website
Guest Editor
Department of Agroforestry Engineering, Higher Polytechnic Engineering School, Campus Terra, University of Santiago de Compostela, 27002 Lugo, Spain
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
Dr. Roberto Besteiro

E-Mail Website
Guest Editor
Centro de Investigacións Agrarias de Mabegondo, Axencia Galega da Calidade Alimentaria Xunta de Galicia, 15318 A Coruña, Spain
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 250 words) can be sent to the Editorial Office for assessment.

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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Published Papers (4 papers)

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Research

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23 pages, 2608 KB  
Article
Designing Predictive Models: A Comparative Evaluation of Machine Learning Algorithms for Predicting Body Carcass Fat in Ewes at Weaning
by Ahmad Shalaldeh, Mosleh Abualhaj, Ahmad Adel Abu-Shareha, Ayman Elshenawy, Yassen Saoudi, Muzammil Hussain, Ahmad Shubita, Majeed Safa and Chris Logan
Agriculture 2026, 16(4), 488; https://doi.org/10.3390/agriculture16040488 - 22 Feb 2026
Viewed by 653
Abstract
Accurate estimation of Body Carcass Fat (BCF) is essential for evaluating the physiological condition of ewes. Traditional assessment via Body Condition Score (BCS) through palpation is inaccurate and subjective. BCF can now be predicted more precisely using objective measurements. This study presents a [...] Read more.
Accurate estimation of Body Carcass Fat (BCF) is essential for evaluating the physiological condition of ewes. Traditional assessment via Body Condition Score (BCS) through palpation is inaccurate and subjective. BCF can now be predicted more precisely using objective measurements. This study presents a comparative analysis of eight machine learning (ML) models for predicting BCF in Coopworth ewes, using weight and RGB-image-based body measurements. Four non-linear regression methods and four neural network architectures were evaluated using a dataset of 74 ewes with 13 independent variables. The dataset was partitioned into training (52 ewes), validation (11 ewes), and testing (11 ewes) sets. The Gradient Boosting Regression achieved the highest predictive accuracy with an R2 value of 0.9434 using body weight and width, followed by Ensemble Neural Network (R2 = 0.9371) using body weight. The findings demonstrate the effectiveness of the Gradient Boosting Regression, Ensemble Neural Network and Random Forest tree-based approaches for morphometric prediction tasks in biological applications. BCF values obtained from image analysis were validated against those derived from computerized tomography (CT), considered the gold standard. These findings highlight the potential of image-guided, ML-driven models for objective, non-invasive, cost-effective assessment of ewe body composition in modern livestock systems. Full article
(This article belongs to the Special Issue Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting to Environmental Control)
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14 pages, 1185 KB  
Communication
PLF-Mamba: Analyzing Individual Milk Yield Dynamics Under Data Scarcity Using Selective State Space Models
by Jonghyun Kim and Chae-Bong Sohn
Agriculture 2026, 16(3), 389; https://doi.org/10.3390/agriculture16030389 - 6 Feb 2026
Viewed by 432
Abstract
Real-world dairy farming datasets are often noisy (e.g., missing or corrupted sensor signals) and contain only short labeled sequences, making conventional correlation analysis and feature prioritization unreliable. We present a robust learning framework that identifies head-specific informative sensor features and predicts daily milk [...] Read more.
Real-world dairy farming datasets are often noisy (e.g., missing or corrupted sensor signals) and contain only short labeled sequences, making conventional correlation analysis and feature prioritization unreliable. We present a robust learning framework that identifies head-specific informative sensor features and predicts daily milk yield by combining reinforcement learning (RL)-based dynamic feature gating with the Mamba architecture. The RL policy samples a binary feature mask to suppress uninformative or corrupted signals to maximize prediction reward, while the Mamba predictor captures long-range dependencies with linear computational complexity. Experiments using the MMCows dataset demonstrate that the proposed framework achieves an average R2 of 0.656 and exhibits substantially lower head-wise variance than Transformer-based baselines, indicating robustness to individual heterogeneity. Ablations removing key components show that RL-based gating is essential: removing the gating module (No-RL) collapses (R2<0). Overall, the proposed approach provides a practical solution for digital livestock farming that mitigates noise and data scarcity while improving robustness across heads. Full article
(This article belongs to the Special Issue Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting to Environmental Control)
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18 pages, 8446 KB  
Article
Evaluation of Single-Shot Object Detection Models for Identifying Fanning Behavior in Honeybees at the Hive Entrance
by Tomyslav Sledevič
Agriculture 2025, 15(15), 1609; https://doi.org/10.3390/agriculture15151609 - 25 Jul 2025
Cited by 1 | Viewed by 1615
Abstract
Thermoregulatory fanning behavior in honeybees is a vital indicator of colony health and environmental response. This study presents a novel dataset of 18,000 annotated video frames containing 57,597 instances capturing fanning behavior at the hive entrance across diverse conditions. Three state-of-the-art single-shot object [...] Read more.
Thermoregulatory fanning behavior in honeybees is a vital indicator of colony health and environmental response. This study presents a novel dataset of 18,000 annotated video frames containing 57,597 instances capturing fanning behavior at the hive entrance across diverse conditions. Three state-of-the-art single-shot object detection models (YOLOv8, YOLO11, YOLO12) are evaluated using standard RGB input and two motion-enhanced encodings: Temporally Stacked Grayscale (TSG) and Temporally Encoded Motion (TEM). Results show that models incorporating temporal information via TSG and TEM significantly outperform RGB-only input, achieving up to 85% mAP@50 with real-time inference capability on high-performance GPUs. Deployment tests on the Jetson AGX Orin platform demonstrate feasibility for edge computing, though with accuracy–speed trade-offs in smaller models. This work advances real-time, non-invasive monitoring of hive health, with implications for precision apiculture and automated behavioral analysis. Full article
(This article belongs to the Special Issue Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting to Environmental Control)
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Review

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26 pages, 2826 KB  
Review
Research Progress of Robotic Technologies and Applications in Smart Pig Farms
by Luyang Zhou, Linqiu Hao, Yingjun Xiong, Huanhuan Qin, Aoran Bao and Zikang Chen
Agriculture 2026, 16(3), 334; https://doi.org/10.3390/agriculture16030334 - 29 Jan 2026
Viewed by 1504
Abstract
With the rapid development of artificial intelligence (AI), the Internet of Things (IoT), and robotics technology, the intelligent transformation of pig farms has become an inevitable trend in the livestock industry. In today’s large-scale pig farms, the traditional breeding methods are undergoing significant [...] Read more.
With the rapid development of artificial intelligence (AI), the Internet of Things (IoT), and robotics technology, the intelligent transformation of pig farms has become an inevitable trend in the livestock industry. In today’s large-scale pig farms, the traditional breeding methods are undergoing significant transformation due to the application of intelligent robotics technology. The robotic system is capable of performing autonomous inspection, precise feeding and environmental cleaning, which can effectively alleviate labor shortages on farms. It also shows great advantages in strengthening biosecurity, optimizing management processes and ensuring animal welfare. This paper systematically constructs the key technical framework of pig farm robots, including the basic support layer, the perception and execution layer, the intelligent processing layer, and the integrated application layer. On this basis, further analysis is conducted on the current application of robots in intelligent pig farms, covering the functional characteristics and technical implementations of inspection robots, cleaning robots, and feeding robots. Full article
(This article belongs to the Special Issue Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting to Environmental Control)
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