Agriculture is an important sector that feeds the economy and creates food security; however, farmers face challenges with access to timely information, proper crop management, and insufficient tools for early disease detection. This project describes a Smart Agriculture Assistant system consisting of a chatbot, database, and image analysis system that allows a farmer to engage with each system as a single point of contact. An ‘AI’ Chatbot that utilizes a text generation API provides farmers with immediate feedback to questions on growing practices, fertilizers, pesticides, and irrigation. A structured crop management database supported by Flask and MySQL, captures detailed information about the crop, including fertilizer, pesticides, pest problems, and growth stage of the crop, and has an admin panel for editing purposes. An image analysis system powered by convolutional neural network (CNN) models primary functions to identify plant disease using the leaf image of the crop. All three systems work together to improve predicaments, limit the amount of loss, and establish sustainable agriculture.
", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2025-11-19", "publisher": "Zenodo", "references": [ { "reference": "1.\tManogna, R. L., Dharmaji, V., & Sarang, S. (2025). Enhancing agricultural commodity price forecasting with deep learning. Scientific Reports, 15, 20903. https://doi.org/10.1038/s41598-025-05103-z" }, { "reference": "2.\tTheofilou, P., & Arampatzis, A. (2025). AI-based agricultural chatbots for sustainable farming: A systematic review. Computers and Electronics in Agriculture, 213, 108\u2013116. https://doi.org/10.1016/j.compag.2025.108116" }, { "reference": "3.\tGururaj, K., & Patil, R. (2023). Smart farming system using machine learning for crop recommendation and yield prediction. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(5), 145\u2013151. https://ijsrd.com/IJSRCSEIT/paper-2023-09-05-145" }, { "reference": "4.\tSable, S., & Jadhav, R. (2024). Integration of AI chatbots and CNN for crop disease detection and farmer assistance. Journal of Emerging Technologies and Innovative Research, 11(1), 312\u2013318. https://www.jetir.org/view?paper=JETIR2401312" }, { "reference": "5.\tAijaz, A., & Singh, M. (2025). AI-powered smart agriculture systems for sustainable crop management. IEEE Access, 13, 45210\u201345219. https://doi.org/10.1109/ACCESS.2025.4521019" }, { "reference": "6.\tPal, S., & Dutta, S. (2024). Lightweight CNN architecture for real-time plant disease classification. Applied Artificial Intelligence, 38(2), 155\u2013167. https://doi.org/10.1080/08839514.2024.1054237" }, { "reference": "7.\tReddy, B., & Sharma, K. (2025). Chatbot-based advisory system for precision farming using natural language processing. International Journal of Engineering Research and Technology, 14(4), 765\u2013772. https://www.ijert.org/chatbot-based-advisory-system-precision-farming" }, { "reference": "8.\tPatel, V., & Naik, D. (2025). Enhancing farmer decision support systems through AI-based chatbot and image processing. International Research Journal of Modernization in Engineering Technology and Science, 7(2), 59\u201365. https://www.irjmets.com/paper/enhancing-farmer-decision-support-systems" }, { "reference": "9.\tLakshmi Reddy, L., Mahitha, K., & Swathi, G. (2024). Agriculture helper chatbot AI. International Journal of Engineering Research and Science & Technology, 20(2), 101\u2013108. https://ijerst.org/article/view/310" }, { "reference": "10.\tAnusha, K., Lakshmi, K. N., & Venkata, V. P. (2025). Smart chatbot for farmers' agricultural guidance and assistance. International Journal of Communication Networks and Information Security, 17(3), 95\u2013104. https://www.ijcnis.org/article/view/8100" } ], "resource_type": { "id": "publication-article", "title": { "de": "Zeitschriftenartikel", "en": "Journal article" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "Smart Agriculture, Chatbot, Crop Management, Plant Disease Detection, Convolutional Neural Network (CNN), Flask, MySQL, Deep Learning, Precision Farming, Sustainable Agriculture." } ], "title": "Smart Agriculture Assistant with Chatbot and Disease Detection" }, "parent": { "access": { "owned_by": { "user": "109378" }, "settings": { "accept_conditions_text": null, "allow_guest_requests": false, "allow_user_requests": false, "secret_link_expiration": 0 } }, "communities": { "default": "0970e40f-6690-4324-b6e8-9a67ecd7a820", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_submission_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2020-06-26T10:34:55.381473+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "0970e40f-6690-4324-b6e8-9a67ecd7a820", "links": {}, "metadata": { "curation_policy": "", "page": "Our journals are peer reviewed, each of which broadly contributes to
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