Deep Learning-Based Multimodal System for Early Detection Of Livestock Diseases Using Image and Audio Fusion

Abstract

Livestock health monitoring is one of the most crucial yet underserved areas in agriculture. Timely detection of diseases can prevent economic losses and safeguard food security. Manual observation methods are subjective, time-consuming, and error-prone. This paper proposes a Deep Learning-Based Multimodal Framework that combines image and audio data to detect animal diseases automatically. The proposed system utilizes Convolutional Neural Networks (CNN) for visual analysis of disease symptoms and Bidirectional Long ShortTerm Memory (BiLSTM) networks for acoustic pattern recognition. Feature-level fusion integrates both modalities to improve accuracy and robustness. The system is designed as a costeffective, scalable, and fully software-based solution suitable for deployment on local or cloudbased platforms. Theoretical analysis suggests that the proposed framework can achieve high accuracy in classifying diseases using only vision and sound modalities.