Cropcare-RAG Chatbot: A Knowledge-Grounded Chatbot for Farmer Queries
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Cropcare-RAG Chatbot: A Knowledge-Grounded Chatbot for Farmer Queries
Nishmitha K
Department of Computer Science and Engineering Ramaiah Institute of Technology
Email: nishmithanishu77@gmail.com
Dr Dayananda R.B
Department of Computer Science and Engineering Ramaiah Institute of Technology
Email: dayanandarb@msrit.edu
Abstract—Agriculture plays a crucial role in ensuring food security and sustaining rural livelihoods; however, farmers fre- quentlyface difficulties in accessing accurate, timely, and reli- ableinformation related to crop diseases and their management. Old farming advice tools plus generic language models usually lack deep farm-specific knowledge, carry stale facts, sometimes invent answers making them shaky for actual field use. This study introduces CropCare-RAG: anagriculture helper built on verified data, combining photo analysis of sick leaves with smart textbacked replies. Instead of guessing, it uses CLIP a mix of sight and words to detectplant issues from pictures. Information comes through a search method powered by BM25, pulling trusted documents into view before any answer forms. That gathered detail shapes what the big language engine says, so replies stay tied to proof, fit the situation, actually help those working the land. Testing happened with real rice disease questions, where spotting symptoms came before advice on handling illnesses. Responses turned out trustworthy, clear, showing fewer made- up details than regular chatbots give. Mixing image analysis with fact-checked data pulls created something usable across many farming areas. Ideas tied together here involve search- backed answers, big text predictors, visual-text tools, sick plant leaf scans,clever farm methods, help systems for growers.
Keywords—Retrieval-Augmented Generation, Large Language Models, vision–language models, crop leaf disease detection, smart agriculture, agricultural advisory systems.
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