ArticleAnimals : an open access journal from MDPI2024
Optimized Machine Learning Models for Predicting Core Body Temperature in Dairy Cows: Enhancing Accuracy and Interpretability for Practical Livestock Management.
Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Physiological Adaptations and Thermoregulatory Indicators of Heat Stress in Bovine Production Systems.Animals : an open access journal from MDPI · 2026Article
- Farm-Level Biosecurity and Antimicrobial Use in Pig Production: An Integrated Inferential and Explainable Machine Learning Analysis.Veterinary sciences · 2026Article
- A Two-Stage Deep Learning Method for Non-Invasive Sow Body Temperature Prediction Fusing Thermal Imaging and Environmental Parameters.Animals : an open access journal from MDPI · 2026Article
- Integrating Taguchi design and machine learning models for trait stability and predicting forage quality in naturally occurring grass pea (Lathyrus spp.).BMC plant biology · 2026Article
- Machine Learning and SHapley Additive exPlanation-Based Interpretation for Predicting Mastitis in Dairy Cows.Animals : an open access journal from MDPI · 2026Article
- From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026Review
- Ocular infrared thermography combined with stacked ensemble machine learning enables contact-free rectal temperature prediction in Murrah buffaloes.Frontiers in veterinary science · 2026Article
- Article
- The Impact of Heat Stress on Dairy Cattle: Effects on Milk Quality, Rumination Behaviour, and Reticulorumen pH Response Using Machine Learning Models.Biosensors · 2025Article
- The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review.Animals : an open access journal from MDPI · 2025Review
- Application of Machine Learning Models for the Early Detection of Metritis in Dairy Cows Based on Physiological, Behavioural and Milk Quality Indicators.Animals : an open access journal from MDPI · 2025Article
- Heat Stress in Dairy Cows: Impacts, Identification, and Mitigation Strategies-A Review.Animals : an open access journal from MDPI · 2025Review
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Heat stress poses a significant challenge to livestock farming, particularly affecting the health and productivity of high-yield dairy cows. This study develops a machine learning framework aimed at predicting the core body temperature (CBT) of dairy cows to enable more effective heat stress management and enhance animal welfare. The dataset includes 3005 records of physiological data from real-world production environments, encompassing environmental parameters, individual animal characteristics, and infrared temperature measurements. Employed machine learning algorithms include elastic net (EN), artificial neural networks (ANN), random forests (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and CatBoost, alongside several optimization algorithms such as Bayesian optimization (BO) and grey wolf optimizer (GWO) to refine model performance through hyperparameter tuning. Comparative analysis of various feature sets reveals that the feature set incorporating the average infrared temperature of the trunk (IRTave_TK) excels in CBT prediction, achieving a coefficient of determination (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.