International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Based Farmer Query Support and Advisory System |
|---|---|
| ABSTRACT | Agriculture remains one of the most essential sectors in developing countries, providing livelihood to millions of farmers and contributing significantly to national food security. However, despite technological advancements in various fields, the agricultural sector still faces critical challenges such as unpredictable weather patterns, pest infestations, improper fertilizer usage, low productivity, and lack of timely expert advice. This paper presents an AI-Based Farmer Query Support and Advisory System designed to bridge the communication gap between farmers and experts by delivering reliable information instantly through mobile devices, chatbots, or web applications. The system takes farmer queries in text or voice form and processes them using CNN techniques to understand intent, then retrieves the most appropriate advisory from a comprehensive agricultural knowledge base. The system tailors responses based on crop type, growth stage, geographical location, and weather conditions. Experimental evaluation on a dataset of 10,000 labeled agricultural queries demonstrates that the CNN-based intent classifier achieves an overall accuracy of 91.4%, with a weighted F1-score of 0.903, outperforming SVM (84.2%) and Naive Bayes (78.6%) baselines. The system responds to queries in 1–3 seconds, confirming its suitability for real-time deployment in rural agricultural advisory scenarios. |
| AUTHOR | JAYANTH G M, JEEVAN GOUDA G G, PRAFUL G KHIMAVATH, RAGHAV GOWDA, PROF. ARCHANA K N UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404097 |
| pdf/97_AI-Based Farmer Query Support and Advisory System.pdf | |
| KEYWORDS | |
| References | 1. ResearchGate, "AI-Powered Agriculture Chatbots for Farmers," https://www.researchgate.net/publication/381003040 2. IJERT, "AI-Based Farming Chatbot with Voice Assistance Support," https://www.ijert.org/research/ai-based-farming-chatbot-with-voice-assistance-support-IJERTCONV13IS05028.pdf 3. IJRASET, "An AI-Based Multilingual Chatbot for Agricultural Assistance," https://www.ijraset.com/research-paper/an-ai-based-multilingual-chatbot-for-agricultural-assistance 4. arXiv, "AI in Agricultural Advisory Systems," https://arxiv.org/abs/2409.08916 5. IJRTE, "CNN-based Query Processing for Agricultural Systems," https://www.ijrte.org/wp-content/uploads/papers/v8i2S5/B10370682S519.pdf 6. JES Publication, "AI-Driven Smart Farming Solutions," https://www.jespublication.com/uploads/2025-V16I6098.pdf |
