Authors : Shital A. Wakchaure; Nikita Chavan; Dr. Manisha Bharti

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/vfbm3u9s

Scribd : https://tinyurl.com/2td85sat

DOI : https://doi.org/10.38124/ijisrt/26May1745

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : India has one of the largest livestock populations globally, and livestock health plays a critical role in supporting rural livelihoods and agricultural sustainability. Delayed disease identification, limited veterinary accessibility, and lack of early diagnostic support often result in economic losses for farmers. This paper presents LAHMS (Livelihood Animal Health Monitoring System), an end-to-end AI-driven framework for visual livestock disease diagnosis, multilingual farmer assistance, and community outbreak monitoring. The proposed system enables farmers to upload images of infected animals and optionally provide symptom descriptions in their native language through a smartphone interface. LAHMS employs a fine-tuned EfficientNetB3 Convolutional Neural Network trained on a custom dataset of 3,300 livestock images covering 11 disease categories across cows, buffaloes, and goats. The classification output is integrated with a LangGraph-based multiagent reasoning pipeline consisting of five coordinated agents for disease analysis, knowledge retrieval, severity assessment, treatment planning, and diagnostic report generation.

Keywords : Livestock Disease Detection, Convolutional Neural Network, EfficientNetB3, LangGraph, Agentic AI, Transfer Learning, Multilingual NLP, Animal Health Monitoring, Streamlit.

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