Fusion of Visual and IoT Data for Livestock Behavior Recognition: Enhancing Precision in Animal Health and Welfare Monitoring

The fusion of IoT sensor data and computer vision holds significant potential for advancing livestock behavior recognition systems, improving animal health and welfare management. This research presents a novel approach that integrates real-time visual data from cameras with IoT-based sensor information, such as movement, temperature, and physiological signals, to create a comprehensive behavior monitoring system. By employing machine learning algorithms, specifically deep learning models, the study aims to enhance the accuracy of behavior recognition for key activities such as feeding, resting, social interaction, and early detection of stress or illness. The proposed system leverages data fusion techniques to combine visual and sensor data, providing more precise insights into livestock behavior than traditional single-source methods. Experimental results from real-world farm environments demonstrate the effectiveness of this approach in automating livestock monitoring and improving early intervention strategies for animal health and productivity. This study contributes to the growing field of precision farming, offering a robust solution for improving livestock welfare through advanced IoT and AI technologies.