“AnimalCare: A Multi-Modal AI Based System for Animal Disease Prediction based on Symptoms and Image Analysis”

Abstract: Artificial Intelligence (AI) is a breath of new life in medical care and has been frequently used in the field of veterinary science as an early indication of disease. Many factors influence the effects of the animal diseases. Important among them are the standard of living of farmers, security of food and animal yield in the rural areas set up which have limited access to veterinary care. It's not easy to diagnose it early because signs are not visible and medical attention is not timely.

This paper presents an idea of an AI animal disease detection system, AnimalCare, with a multimodal approach to combine both image-based and symptom-based input. Machine learning algorithms and deep learning models are more effective in terms of prediction accuracy and reliability to perform symptom classification and disease detection based on images respectively.

It is also a system that supports multi-linguals which will improve the access to the system by the rural users and will also offer expert guidance to the users through a doctor consultation module. Also, a chatbot powered by artificial intelligence can provide real-time support on animal health, symptoms and preventive measures. The experimental results show that the proposed system is more specific and applicable than conventional single model methods.

The system will be a realistic and scalable solution for real-world animal healthcare management due to its multi-modal input, interactive features and skilled assistance.

Keywords: Animal Disease Prediction, Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Network, Veterinary Healthcare, Multimodal System