AgriAdvisor Pro: A Multimodal LLM-Driven Precision Advisory System for Enhanced Sustainable Farming

A smallholder farmer in rural Karnataka spots something wrong with her tomato crop. She photographs the leaf, but the nearest agronomist is fifty kilometres away and charges fees she cannot afford. AgriAdvisor Pro is the system we built to close that gap. It pairs a fine-tuned EfficientNet-B2 classifier (97.88% accuracy, 65 classes) with Google Gemini 2.0 Flash for language generation, and stitches them together through a parallel orchestration layer running on Python’s asyncio. In concrete terms, this means advisory turnaround went from about 18 seconds down to 4.2—a difference that matters when your connection drops every few minutes. A FAISS-backed RAG pipeline ties each recommendation to verified regional documents rather than letting the model guess. We tested the system across 127 farms over one kharif season and saw a 34% drop in preventable crop losses along with less indiscriminate pesticide spraying. One season in one state is hardly definitive, and we are aware of that limitation. But even these preliminary numbers hint that designing around the farmer’s real constraints—patchy bandwidth, regional languages, limited digital literacy—can turn AI from a lab curiosity into something genuinely useful on the ground.