Paper Title
Animal Disease Prediction Using Artificial Intelligence and Image Processing

Abstract
Animal illnesses are a major global hazard to agricultural productivity, livestock health, and economic stability. This dissertation investigates the viability and efficacy of forecasting diseases in eight distinct animal species—cows, buffalos, pigs, sheep, goats, chickens, horses, and ducks—by utilizing AI and image processing approaches. Looking at the growing problems of farmers, they are unable to provide the immediate help and aid to their animals which deals in unwanted casualties due to lack of proper service and support. Other literature works provided foundational knowledge, guiding research methodology, dataset selection, algorithm choice, and interpretation of findings, enhancing the dissertation's credibility and relevance. The results of this study show how AI and image processing methods can be used to reliably and accurately forecast animal diseases. The generated models demonstrated good results in the identification of various diseases based on visual data across the eight animal species under consideration. This dissertation concludes by highlighting the significance of using AI and image processing technology to forecast animal diseases. Veterinarians and livestock farmers can improve early illness detection and minimize possible outbreaks and financial losses by utilizing machine learning algorithms and image analysis tools. Keywords - Animal Illnesses, Farmers' Problems, Early Illness Detection, Outbreaks, Financial Losses