Smart Livestock Disease Prediction

Abstract

The productivity of agriculture, food security as well as the rural livelihood is at the risk of ever-increasing outbreaks of livestock diseases across the world, especially in developing countries where veterinary services are not readily accessible. Traditional methods of diagnoses, which are time consuming and prone to error, are still what farmers have to depend on, leading to missed or wrong treatment, causing not just monetary loss but also increased spread of diseases. The challenges are more difficult to overcome due to the lack of timely and accurate diagnostic technologies, particularly in remote and marginalized farming areas. This work addresses these issues by presenting the architecture and design of a Smart Livestock Disease Prediction System based on Artificial Intelligence and Mobile Tech nology to assist in the early and automated detection of cattle diseases. The system is based on a MobileNetV2 convolutional neural network which has been fine-tuned on a trade dataset by using transfer learning, trained on a dataset comprised of region specific cattle diseases, allowing real time image based classification on a mobile app. The method proposed takes an agile development approach that includes the steps of gathering and preprocessing the data, training the model and deploying it in a React Native mobile app with Node.js backend and MongoDB database. Farmers, through the mobile app, can take photos or upload existing rural livestock im ages, and get instant predictions with confidence scores, descriptions of disease, treatment suggestions, and precautions. Veterinarians enhance system accuracy by confirming the AI generated diagnosis and modifying the disease knowledge base. It also includes an interac tive dashboard illustrating livestock health, disease incidence patterns and climatological data to inform early decisions. Connecting farmers directly with expert veterinary advice through an easy to use, AI enabled platform, the system improves disease monitoring, encourages early action, and advocates for sustainable management of livestock. The initiative demonstrates that it is feasible to deploy lightweight deep learning models in resource constrained environments characteristic of the agriculture sector, and advances the emerging field of digital agriculture.