Conceptual Multimodal AI Architecture for the Early Diagnosis of Pig Respiratory Diseases

Abstract

This paper explores the development of a conceptual multimodal hardware and software architecture for the early detection of respiratory diseases in pigs at the PMK-3 pig-breeding complex (LLC Pribaltiyskaya Myasnaya Kompaniya Tri). Modern precision livestock farming (PLF) technologies, including acoustic monitoring, computer vision, and infrared thermography, are considered. Existing monomodal approaches are insufficient for effectively automating the process of registering sick animals. Laboratory models are poorly suited to industrial pig farm conditions and do not support integration with ERP systems, including 1C. The proposed architecture is based on edge devices fitted with microphone arrays and thermal imaging cameras. Cascade analysis (an acoustic trigger activates visual and thermal imaging verification) enables highly accurate identification of sick animals, reducing the risk of false alarms and diagnostic delays. The proposed architecture minimizes computing resource consumption and reduces the load on the network infrastructure by transferring only structured metadata to the «1C: Enterprise 8. Livestock Breeding. Pig Farming» ERP system.