Enhancing Cattle Health Monitoring through ML-Based Disease Detection and AI-Driven Treatment Recommendation

Abstract - Livestock disease detection remains a challenge in dairy farming, with delayed diagnosis leading to economic loss and animal welfare problems. The traditional method relies on manual observation and veterinary visits, which tends to lead to late intervention and higher treatment costs. This paper proposes an AI-driven Disease Detection System incorporating Dairy Care System framework with computer vision and deep learning to identify diseases from cattle images and offer real-time disease diagnosis with confidence rates. Google's API is employed in the system to enhance disease knowledge, such as symptoms,treatment, and prevention. Common diseases of cattle are identified using a neural network trained specifically for that purpose, and a centralized database of health records facilitates pattern recognition and historical analysis. Farmers can quickly upload images to receive instant diagnosis, receive diagnostic reports, and access treatment recommendations. Automated disease identification combined with preventive recommendations reduces the time between noticing symptoms and treatment. Experimental results verify precision in disease identification at an astounding rate, improving management effectiveness by up to 60% compared to traditional veterinary practice, and therefore being of particular utility in isolated farms with limited access to veterinarians.