Design and Evaluation of a Natural Language-Based Chatbot for Smart Farming Advisory Services
DOI:
https://doi.org/10.69996/fmep.2025018Keywords:
Agriculture Chatbot, Natural Language Processing (NLP), n-gram Model, Smart Farming, Precision AgricultureAbstract
Agricultural farming is the backbone of many economies, particularly in rural and developing regions, where it serves as the primary source of livelihood and food production. It involves the cultivation of crops and the rearing of animals to meet both local consumption and market demands. Farming practices vary widely based on climate, soil conditions, technology availability, and cultural traditions, ranging from traditional subsistence farming to modern, mechanized agriculture. This research paper presents the development and evaluation of an intelligent Agriculture Assistant Chatbot designed to support farmers by providing real-time, accurate, and relevant agricultural information through natural language interaction. The chatbot leverages n-gram-based Natural Language Processing (NLP) to understand user queries, identify intent, and extract key entities related to crops, fertilizers, and farming practices. Built with a user-friendly interface and integrated with expert knowledge and a feedback-driven learning mechanism, the system enhances communication between farmers and agricultural advisory services. The architecture allows for multilingual support and incorporates modules for crop and fertilizer details, user registration, and sentiment-based feedback analysis. Experimental results show that out of a total of 83 registered users, 56 users actively logged in, and 62 successful bot interactions were recorded, indicating a strong engagement rate of approximately 74.7%. User satisfaction metrics, derived from the feedback system, showed that 60% of users rated their experience as very positive, 25% expressed neutral sentiment, while only 15% provided negative feedback. Furthermore, analysis of problem-solving efficiency demonstrated that the chatbot-assisted queries had a resolution success rate increase of over 40% compared to those without bot interaction. Response time was consistently below 2 seconds on average, contributing to a seamless user experience.
References
[1] R. Biswas and N. Goel, “Intelligent chatbot assistant in agriculture domain,” In International Conference on Agriculture-Centric Computation, Cham: Springer Nature Switzerland, pp.180-194, 2023.
[2] N. Singh, J. Wang'ombe, N. Okanga, T. Zelenska, J. Repishti, S. Mishra and A. Nambi, “Farmer. Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers,” arXiv preprint arXiv:2409.08916, 2024.
[3] E. Asolo, I. Gil-Ozoudeh and C. Ejimuda, “AI-Powered decision support systems for sustainable agriculture using AI-Chatbot solution,” Journal of Digital Food, Energy & Water Systems, vol.5, no.1, 2024.
[4] V. Piyathilake, T. Dilni, R. Pushpananda, L. De Silva and Y. Zaheed, “Towards a Conversational AI Chatbot to Assist Farmers in Disease Detection,” In EPIA Conference on Artificial Intelligence, Cham: Springer Nature Switzerland, pp.206-217, 2024.
[5] V. Venkatesh, K. Kunju, S. Suresh, S. Natesan and S. Kumar, “AI based farmer assistant talkbot,” In AIP Conference Proceedings, AIP Publishing, vol.2857, no.1, 2023.
[6] P. Singh, M. Kansal, G. Tyagi, K. Tanwar, A. P. Singh and R. K. Yadav, “Empowering Farmers: An AI-Based Solution for Agricultural Challenges,” In Computational Intelligence in Internet of Agricultural Things, Cham: Springer Nature Switzerland, pp.401-417, 2024.
[7] V. Nivi, S. Geerthik, M. Karuppaiya, A. Muthukumaran and S. Tamilarasan, “AgriBot: An Integrated Chatbot Platform for Precision Agriculture and Farmer Support using Deep Learning Techniques,” In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, pp.1-6, 2024.
[8] H. Nikam, M. Bhargava, S. Sasmal, P. Dhote, L. Singh, S. Dhotre, S., ... and A. Thakur, “Enhancing Agricultural Practices with AgroDialogue: A Chatbot Framework,” In 2024 IEEE Pune Section International Conference (PuneCon), pp. 1-11, 2024.
[9] S. Banerjee, P. Akhila, E. Elakiya and B. Surendiran, “CropBot: A Chatbot for Agriculture Advancement,” In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), pp.1-6, 2024.
[10] V. S. Kumar, M. V. Thanmayee, K. V. S. Satwik, P. Gayathri and M. Greeshma, “Agriculture assistant chatbot using CNN,” Journal of Engineering Sciences, vol.14, no.05, 2023.
[11] R. Bhatia and S. Kaur, “Development and Evaluation of a Smart Agricultural Chatbot,” In 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 158-163, 2024.
[12] V. Vanitha, N. Rajathi and K. Prakash Kumar, “AI-Based Agriculture Recommendation System for Farmers,” In Computer Vision and Machine Learning in Agriculture, Singapore: Springer Nature Singapore, vol.3, pp.91-103, 2023.
[13] P. U. Usip, O. R. James, D. E. Asuquo, E. N. Udo and F. B. Osang, “Usability evaluation of an intelligent mobile chatbot for crop farmers,” In International Conference on Electronic Governance with Emerging Technologies, Cham: Springer Nature Switzerland, pp. 101-111, 2023.
[14] A. Ibrahim, K. Senthilkumar and K. Saito, “Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria,” Scientific Reports, vol.14, no.1, pp.3407, 2024.
[15] M. K. Korir, W. Mwangi and M. W. Kimwele, “Artificial Intelligence-Based Chatbot Model Providing Expert Advice to Potato Farmers in Kenya,” In 2023 IEEE AFRICON, pp.1-6, 2023.
[16] P. N. S. S. V. Charishma, S. Rupa Lakshmi and M. Vijaya Durga, “Making Crop Recommendations using Machine Learning Techniques,” Journal of Computer Allied Intelligence, vol.2, no.2, pp.1-12, 2024.
[17] C. L. Mei, W. C. Hoo, P. Shaurya and K. Roy, “Leveraging AI-Driven Chatbots for Sustainable Agricultural Advisory Services: Bridging SDG Innovation Gaps in Rural Malaysia,” International Journal of Environmental Sciences, vol.11, no.8s, pp.410-420, 2025.
[18] Neeta Baporikar, “Strategies to Boost Horticulture Development,” International Journal of Advance Research and Innovation, vol.11, no.4, pp.21-32, 2023.
[19] R. Biswas and D. N. Goel, “Check for updates Intelligent Chatbot Assistant in Agriculture Domain,” In Agriculture-Centric Computation: First International Conference, ICA 2023, Chandigarh, India, May 11-13, 2023, Revised Selected Papers. Springer Nature, pp.180, 2023.
[20] R. J. Ong, R. A. A. Raof, S. Sudin and K. Y. Choong, “Chatbot application training using natural language processing techniques: Case of small-scale agriculture,” In AIP Conference Proceedings. AIP Publishing, vol. 2991, no.1, 2024.
