AI-Enabled Detection and Classification of Cattle Disorders Using Machine Learning.

Timely identification of cattle diseases is vital to prevent productivity losses and ensure better animal health. This work introduces a practical Artificial Intelligence and Machine Learning (AIML)–based framework for recognizing common cattle disorders from photographic images. The proposed system follows a complete workflow that includes image collection and labeling, preprocessing with augmentation, and classification using a lightweight MobileNetV2 model adapted through transfer learning. The trained model is integrated with a web-based API for accessible, real-time diagnosis. Experimental findings indicate that the method achieves reliable accuracy while remaining affordable and easy to deploy in rural settings. In addition, challenges such as limited data availability, model interpretability, and field deployment are discussed. The study demonstrates that an AI-driven approach can support faster and more accurate disease detection, ultimately helping farmers and veterinarians improve livestock welfare and reduce economic loss.