DEEPVISON: AN INTELLIGENT FRAMEWORK FOR LIVESTOCK

Abstract

Deep Vision: An Intelligent Framework for Livestock Monitoring is a smart, low-cost monitoring system designed to enhance livestock health, welfare, and farm productivity, particularly in rural and underserved areas. The system leverages existing mobile camera footage, eliminating the need for expensive IoT devices, and uses the YOLOv8 object detection model to accurately identify and track animals in real time. For behavior analysis, the system employs Convolutional Neural Networks (CNNs), specifically using architectures like EfficientNet and ResNet, to classify key livestock behaviors such as feeding, resting, movement, aggression, and isolation—crucial indicators of animal health and well-being. To ensure robust performance across varying environmental conditions such as lighting, weather, and farm layout, the system uses synthetic data augmentation through Generative Adversarial Networks (GANs), addressing class imbalance and improving generalization. The model also incorporates explainable AI through  Grad-CAM, allowing visualization of the decisionmaking process by highlighting the  regions in an image that influenced the CNN’s predictions—this builds trust, transparency for farmers and experts interpreting system outputs. Furthermore, to make the system accessible to farmers in diverse linguistic regions, it integrates Google Translate API to provide multilingual dashboard support, alerts, and insights in local languages, thereby enhancing usability and inclusiveness. An interactive dashboard offers live animal monitoring, behavior tagging, automated health alerts, and historical behavior analytics, enabling timely and informed decisions. Overall, this system combines deep learning, explainability, and language accessibility to deliver a practical solution.