Realtime Monitoring of Animal Behavior Using Deep Learning Models
Accurate monitoring of animal health and behavior is crucial for improving welfare and productivity in livestock management. Traditional observation methods are time-consuming and prone to subjective bias. To address these challenges, we propose an automated system for behavioral pattern using deep...
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| Format: | Article |
| Language: | English |
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Università degli Studi di Firenze
2025-07-01
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| Series: | Journal of Agriculture and Environment for International Development |
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| Online Access: | https://www.jaeid.it/index.php/jaeid/article/view/16397 |
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| author | Romesa Rao Salman Qadri Rao Kashif |
| author_facet | Romesa Rao Salman Qadri Rao Kashif |
| author_sort | Romesa Rao |
| collection | DOAJ |
| description | Accurate monitoring of animal health and behavior is crucial for improving welfare and productivity in livestock management. Traditional observation methods are time-consuming and prone to subjective bias. To address these challenges, we propose an automated system for behavioral pattern using deep learning-based pose estimation techniques. Specifically, we utilize ResNet-50, a deep convolutional neural network, to detect key anatomical landmarks such as the nose, eyes, ears, and body center. By tracking these keypoints, we generate movement trajectories that help identify behavioral patterns. For behavior classification, we initially applied a decision tree algorithm, achieving an accuracy of 60%. To enhance performance, we implemented a random forest classifier, which significantly improved the accuracy to 96%. The system tries to classify seven key behaviors: "stand," "sit," "eat," "drink," "aggressive," "sit with legs tied," and "let go of the tail." The random forest model achieved the highest accuracy in detecting "standing" and "aggressive" behaviors, while lower accuracy was observed for "eating" behavior. Additionally, our pose estimation model demonstrated high precision and recall metrics, indicating robust performance in keypoint detection with minimal deviation from ground truth annotations. This automated system reduces the need for manual observation and provides a reliable tool for monitoring some animal behavior. The potential applications extend to various domains, including animal studies and livestock management, offering a scalable and user-friendly solution for real-time behavior analysis.
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| format | Article |
| id | doaj-art-3af4b68c4db04d759e8204df5a95b91b |
| institution | Kabale University |
| issn | 2240-2802 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Università degli Studi di Firenze |
| record_format | Article |
| series | Journal of Agriculture and Environment for International Development |
| spelling | doaj-art-3af4b68c4db04d759e8204df5a95b91b2025-08-20T03:50:06ZengUniversità degli Studi di FirenzeJournal of Agriculture and Environment for International Development2240-28022025-07-01119110.36253/jaeid-16397Realtime Monitoring of Animal Behavior Using Deep Learning Models Romesa Rao0Salman Qadri1Rao Kashif2Institute of Computing, Muhammad Nawaz Shareef University of Agriculture, Multan, PakistanInstitute of Computing, Muhammad Nawaz Shareef University of Agriculture, Multan, PakistanFaculty of Engineering & Computing, National University of Modern Languages, PakistanAccurate monitoring of animal health and behavior is crucial for improving welfare and productivity in livestock management. Traditional observation methods are time-consuming and prone to subjective bias. To address these challenges, we propose an automated system for behavioral pattern using deep learning-based pose estimation techniques. Specifically, we utilize ResNet-50, a deep convolutional neural network, to detect key anatomical landmarks such as the nose, eyes, ears, and body center. By tracking these keypoints, we generate movement trajectories that help identify behavioral patterns. For behavior classification, we initially applied a decision tree algorithm, achieving an accuracy of 60%. To enhance performance, we implemented a random forest classifier, which significantly improved the accuracy to 96%. The system tries to classify seven key behaviors: "stand," "sit," "eat," "drink," "aggressive," "sit with legs tied," and "let go of the tail." The random forest model achieved the highest accuracy in detecting "standing" and "aggressive" behaviors, while lower accuracy was observed for "eating" behavior. Additionally, our pose estimation model demonstrated high precision and recall metrics, indicating robust performance in keypoint detection with minimal deviation from ground truth annotations. This automated system reduces the need for manual observation and provides a reliable tool for monitoring some animal behavior. The potential applications extend to various domains, including animal studies and livestock management, offering a scalable and user-friendly solution for real-time behavior analysis. https://www.jaeid.it/index.php/jaeid/article/view/16397Pose EstimationBehavior ClassifierResNet-50Trajectrory AnalysisRandom ForestDecision Tree |
| spellingShingle | Romesa Rao Salman Qadri Rao Kashif Realtime Monitoring of Animal Behavior Using Deep Learning Models Journal of Agriculture and Environment for International Development Pose Estimation Behavior Classifier ResNet-50 Trajectrory Analysis Random Forest Decision Tree |
| title | Realtime Monitoring of Animal Behavior Using Deep Learning Models |
| title_full | Realtime Monitoring of Animal Behavior Using Deep Learning Models |
| title_fullStr | Realtime Monitoring of Animal Behavior Using Deep Learning Models |
| title_full_unstemmed | Realtime Monitoring of Animal Behavior Using Deep Learning Models |
| title_short | Realtime Monitoring of Animal Behavior Using Deep Learning Models |
| title_sort | realtime monitoring of animal behavior using deep learning models |
| topic | Pose Estimation Behavior Classifier ResNet-50 Trajectrory Analysis Random Forest Decision Tree |
| url | https://www.jaeid.it/index.php/jaeid/article/view/16397 |
| work_keys_str_mv | AT romesarao realtimemonitoringofanimalbehaviorusingdeeplearningmodels AT salmanqadri realtimemonitoringofanimalbehaviorusingdeeplearningmodels AT raokashif realtimemonitoringofanimalbehaviorusingdeeplearningmodels |