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The research aims to develop a low-cost, intelligent monitoring system to enhance livestock health and productivity.

May 21, 2026Open Access

Deepvison: An Intelligent Framework for Livestock

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The research aims to develop a low-cost, intelligent monitoring system to enhance livestock health and productivity.
Leveraged mobile camera footage for real-time animal tracking using the YOLOv8 model.
Utilized CNN architectures like EfficientNet and ResNet for behavior classification.
Implemented data augmentation with GANs to improve model performance under varying conditions.
Accurate identification and tracking of livestock behaviors improved health monitoring outcomes.
The system's explainable AI features increased user trust in decision-making processes.
Multilingual support enhanced accessibility for diverse farmer populations.

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Randomized trial demonstrates enhanced livestock health monitoring in rural areas, suggesting a practical solution for farmers.

Prakshiptha et al. (2026) studied this question.

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