A Deep Learning Framework for Early Detection and Treatment of Livestock Skin Diseases

India’s livestock sector, a backbone of rural livelihoods and food production, continues to suffer significant setbacks due to delayed and inaccurate disease diagnosis. The increasing prevalence of skin infections, zoonotic diseases, and outbreaks like lumpy skin disease (LSD) has underscored the urgent need for scalable, technology-driven solutions. This paper proposes an intelligent livestock health monitoring and advisory system designed to address these gaps using deep learning techniques. The system integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) models, and the ResNet50 architecture to accurately classify common skin conditions in livestock based on image inputs. Leveraging methods such as data augmentation, transfer learning, and ensemble modeling, the platform improves detection accuracy even in diverse rural environments. Beyond diagnosis, it delivers instant treatment recommendations—including suggested medications and nutritional plans—tailored to each disease. By equipping farmers with early, actionable insights, the system offers a pathway to reduce mortality, mitigate economic losses, and strengthen disease surveillance. This approach aligns with national priorities for improving veterinary outreach, preventing rural distress, and ensuring food and income security for millions dependent on animal husbandry.