Cows Archives - Tech4Animals https://tech4animals.haifa.ac.il/publication-field/cows/ Animal-centered AI that SEES Animals Sat, 30 May 2026 13:05:45 +0000 en-US hourly 1 https://wordpress.org/?v=7.0.2 https://tech4animals.haifa.ac.il/wp-content/uploads/2025/07/cropped-FAV-32x32.png Cows Archives - Tech4Animals https://tech4animals.haifa.ac.il/publication-field/cows/ 32 32 AI-based Prediction and Detection of Early-onset of Digital Dermatitis in Dairy Cows using Infrared Thermography https://tech4animals.haifa.ac.il/publications/ai-based-prediction-and-detection-of-early-onset-of-digital-dermatitis-in-dairy-cows-using-infrared-thermography/ Sat, 03 Aug 2024 06:54:25 +0000 https://tech4animals.haifa.ac.il/?post_type=publications&p=2553 This study applied deep learning-based computer vision techniques using infrared thermography (IRT) data for the early detection and prediction of Digital Dermatitis (DD) in dairy cows, a common foot disease that negatively impacts animal welfare, milk production, and fertility. The researchers investigated the role of various inputs, including thermal images of cow feet, statistical color […]

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]]> This study applied deep learning-based computer vision techniques using infrared thermography (IRT) data for the early detection and prediction of Digital Dermatitis (DD) in dairy cows, a common foot disease that negatively impacts animal welfare, milk production, and fertility. The researchers investigated the role of various inputs, including thermal images of cow feet, statistical color features extracted from IRT images, and manually registered temperature values. Their models achieved an accuracy of above 81% for DD detection on ‘day 0’ (the first appearance of clinical signs) and above 70% accuracy for predicting DD two days prior to clinical signs. The findings indicate that combining IRT images with AI-based predictors shows significant potential for developing future real-time automated tools for monitoring DD in dairy cows, which could lead to improved DD management, more rapid treatments, enhanced animal wellbeing, and reduced negative effects on lactation and reproductive performance. While the study demonstrated promising results, it noted limitations such as a suboptimal number of cows, suggesting that an increased number of monitored animals would likely lead to higher performance for both detection and prediction models.

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]]> BovineTalk: Machine learning for vocalization analysis of dairy cattle under the negative affective state of isolation https://tech4animals.haifa.ac.il/publications/bovinetalk-machine-learning-for-vocalization-analysis-of-dairy-cattle-under-the-negative-affective-state-of-isolation/ Fri, 16 Aug 2024 20:22:38 +0000 https://tech4animals.haifa.ac.il/?post_type=publications&p=2758 This study addresses the critical need for developing non-invasive, animal-based indicators of affective states in livestock, specifically focusing on vocalizations in dairy cattle. The study’s primary contribution is providing the largest pre-processed dataset to date of vocalizations from 20 lactating adult multiparous dairy cows, collected under a controlled setting during negative affective states induced by […]

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]]> This study addresses the critical need for developing non-invasive, animal-based indicators of affective states in livestock, specifically focusing on vocalizations in dairy cattle. The study’s primary contribution is providing the largest pre-processed dataset to date of vocalizations from 20 lactating adult multiparous dairy cows, collected under a controlled setting during negative affective states induced by visual isolation. Utilizing this dataset, the researchers developed two computational frameworks—a deep learning-based model and an explainable machine learning-based model—for two key tasks: classifying high-frequency (HF) and low-frequency (LF) cattle calls, and identifying individual cows based on their vocalizations. Their models achieved high accuracy in classifying LF and HF calls, reaching 87.2% for the explainable model and 89.4% for the deep learning model, outperforming previous state-of-the-art approaches and exhibiting less overfitting. For individual cow identification, the models demonstrated 68.9% accuracy with the explainable model and 72.5% with the deep learning model, with HF calls containing more individuality information than LF calls. The study also identified important vocal features for these classifications, such as AMvar, AMrate, AMExtent, Formant dispersal, and Wiener entropy mean for call type, and sound duration for individual identification. These results underscore the potential of machine learning approaches in analyzing cattle vocalizations as a valuable tool for assessing emotional valence and informing precision livestock farming practices.

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]]> Machine Learning Approaches to Predict and Detect Early-onset of Digital Dermatitis in Dairy Cows using Sensor Data https://tech4animals.haifa.ac.il/publications/machine-learning-approaches-to-predict-and-detect-early-onset-of-digital-dermatitis-in-dairy-cows-using-sensor-data/ Wed, 16 Aug 2023 20:40:56 +0000 https://tech4animals.haifa.ac.il/?post_type=publications&p=2762 This study explored the use of machine learning algorithms based on sensor behavior data for the early-onset detection and prediction of Digital Dermatitis (DD) in dairy cows. Researchers utilized CowManager® ear tags to continuously record data such as activity, eating time, rumination time, and ear temperature from Holstein cows at the Washington State University Knott […]

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]]> This study explored the use of machine learning algorithms based on sensor behavior data for the early-onset detection and prediction of Digital Dermatitis (DD) in dairy cows. Researchers utilized CowManager® ear tags to continuously record data such as activity, eating time, rumination time, and ear temperature from Holstein cows at the Washington State University Knott Dairy Center. The machine learning model, based on the Tree-Based Pipeline Optimization Tool (TPOT), achieved an accuracy of 79% for DD detection on the day clinical signs appeared. For predicting DD 2 days prior to the first clinical signs, a combination of K-means and TPOT reached an accuracy of 64%. The study identified ‘activity’ as the most important sensor feature for DD detection. The proposed models demonstrate the potential for developing real-time automated tools for monitoring and diagnosing DD in lactating dairy cows, suggesting that alterations in behavioral patterns can serve as inputs for early warning systems to improve herd management and animal welfare. While promising, the authors note limitations, including the number of cases and the need for further studies in diverse farming settings with potentially more complex sensor systems.

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]]> Comparing the Performance of Deep Learning Video-based Models and Trained Veterinarians in Cattle Pain Assessment https://tech4animals.haifa.ac.il/publications/comparing-the-performance-of-deep-learning-video-based-models-and-trained-veterinarians-in-cattle-pain-assessment/ Wed, 18 Mar 2026 13:30:54 +0000 https://tech4animals.haifa.ac.il/?post_type=publications&p=2976 Accurately assessing pain in cattle remains a significant challenge because these prey animals naturally conceal signs of discomfort, which can lead to subjective and highly variable human evaluations. To address this limitation, this study introduced a novel, video-based deep learning framework designed to automatically detect subtle pain-related expressions and behavioral changes. Using computer vision techniques […]

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]]> Accurately assessing pain in cattle remains a significant challenge because these prey animals naturally conceal signs of discomfort, which can lead to subjective and highly variable human evaluations. To address this limitation, this study introduced a novel, video-based deep learning framework designed to automatically detect subtle pain-related expressions and behavioral changes. Using computer vision techniques to analyze temporal and spatial features in video recordings of bulls, the system’s performance was compared against standardized scoring by trained veterinarians. The machine learning model achieved an impressive 97% accuracy in pain classification. This performance successfully outperformed video-based human assessments and matched the accuracy of real-time expert evaluations, offering a highly objective and scalable tool for monitoring livestock welfare.

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