Conventional animal health management practices involve extensive manual observation and documentation, resulting in late disease detection and ineffective veterinary care, especially in rural areas. To fill this pressing need, this paper proposes a comprehensive AI-assisted web application for proactive animal health monitoring. The proposed system employs a strong three-tier architecture, combining a React.js front end, a Node.js API gateway, and Supabase for secure and real-time data management. The system is segmented into role-based portals for Farmers, Veterinarians, and Administrators, supporting bilingual functionality (English and Tamil) for broad grassroots reach. The key innovation here is the combination of two Artificial Intelligence components: a Convolutional Neural Network (CNN) for the quick diagnosis of dermatological and visible diseases from user-submitted images and a Natural Language Processing (NLP) engine that combines unrefined farmer observations into formatted clinical reports. By leveraging the digital recording of longitudinal vitality parameters such as temperature and food intake, along with AI-driven diagnoses, the proposed system enables precise remote veterinary diagnosis. This system greatly minimizes the time gap between disease manifestation and treatment, thus enhancing animal well-being, preventing economic losses for farmers, and optimizing the workflow of veterinary experts.
R et al. (Thu,) studied this question.