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 the primary source of income for many developing countries, but farmers have enduring challenges such as climate unpredictability, crop diseases, water mismanagement, and inadequate tailored guidance. This paper presents a smart AI-powered web application developed specifically for small and marginal farmers that enables efficient farming decision-making without requiring expensive IoT gadgets. Important aspects are included such as a crop recommendation system, image-based disease and pest recognition, smart irrigation, weather forecasting, and answer chatbots that respond to various farming queries. It can take input in different forms, supporting various languages, and even voice commands for those who are illiterate or uneducated. The solution is scientifically and economically tested, user-friendly, and accessible to all intended users. The application is constructed in Python and employs real-time AI models tested extensively for precision. More features are planned in the future, such as AI-driven market price forecasting, personal dashboards for farmers, AI-driven crop rotation scheduling, entrepreneur networks for farmers, an AI-driven subsidy and loan advisory system, a digital crop calendar with AI, and numerous other features. |
| AUTHOR | V. PRIYADHARSHINI, R. GAYATHRI, MOUNIKA.V Dept. of Master of Computer Applications, Vivekanandha Institute of Information and Management Studies, Tiruchengode, Tamil Nadu, India Assistant Professor, Dept. of Master of Computer Applications, Vivekanandha Institute of Information and Management Studies, Tiruchengode, Tamil Nadu, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406065 |
| pdf/65_AI- Based Farmer Query Support and Advisory System.pdf | |
| KEYWORDS | |
| References | [1]. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. [2]. Hossain, M. A. (2021). Crop disease detection using machine learning: A review. [3]. Science and Technology Reports, 3(1), 1–8. [4]. Gnanasekaran, T. N., & Chatterjee, S. (2020). AIbased farmer advisory system using chatbot technology. Journal of Artificial Intelligence and Soft Computing Research, 9(2), 87–95. [5]. Khivsara, M. P., & Kotecha, R. P. (2018). Weather forecasting using machine learning algorithms. International Journal of Computer Applications, 179(27), 20– 24. [6]. Bulla, R. W., & Patil, M. B. (2020). Smart irrigation system using AI. International Journal of Engineering Research & Technology (IJERT), 9(5), 143–146. [7]. Government of India, Ministry of Agriculture and Farmers Welfare. (2021). Digital Agriculture: National Strategy Document. [8]. Crop Water Requirement Dataset, Kumar, P. (2022). Crop Water Requirement. Kaggle. [9]. Crop Recommendation Dataset, Ingle, A. (2022). Crop Recommendation Dataset. Kaggle. [10]. PlantVillage Dataset, Abdallah, A. (2023). PlantVillage Dataset. Kaggle. [11]. OpenRouter API ( Meta L L a M A -3-8B I n s t r u c t ), O p e n R o u t e r T e a m . (2024). [12]. OpenRouter API – Access to LLaMA and other LLMs. [13]. OpenWeatherMap API, OpenWeather Ltd. (2024). OpenWeatherMap – Weather Data API. [14]. CNN Classifier (MobileNetV2), Howard, A.G., et al. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. [15]. Random Forest Classifier, Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. [16]. Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. [17]. Ramesh, T. (2021). AI-based smart irrigation system for sustainable agriculture. [18]. Journal of Ambient Intelligence and Humanized Computing, 12, 8357–8367. [19]. Kumar, N., & Bhatia, P. K. (2014). A detailed review of the crop disease detection using image processing. International Journal of Advanced Research in Computer Science and Software Engineering, 4(7), 456–458. [20]. Dhanya, V. S., & Kumar, V. P. (2021). A machine learning-based approach for crop selection and yield prediction in Indian agriculture. Procedia Computer Science, 184, 15–22. [21]. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. |
