Choosing what to grow means looking at soil, water, weather and prices together, yet farmers usually have to search for each of these in a different place. AgriNexus, a Farmer Helper Chatbot created for Karnataka, brings crop recommendations, weather updates and market prices into one web application. The server is written in Python with Flask, the pages are built with HTML and CSS, SQLite stores structured records, and Pandas loads the price data. A farmer enters the district, taluk, village, land area, soil type and water availability; the system then scores a set of crops using hand-written rules and lists the top matches with short explanations. Weather data is fetched from the OpenWeather API, and prices are read from a CSV file. No machine-learning model has been trained yet, so the quality of the recommendations depends on how well the rules match real conditions, the weather display depends on the external service being reachable, and the price display is only as up to date as the CSV file. The tool is meant for learning and early planning, not as a replacement for advice from agricultural experts. We outline the motivation, related work, design, method, implementation, a testing plan, limitations and future directions. No performance figures are reported because a formal evaluation has not yet been conducted.

Maseeha Bhanu, Akshatha M M, Impana R H, R Deepika Patil, Yashaswini M N, "AgriNexus: An Intelligent Framework for Weather-Based Crop Recommendation and Agricultural Market Analysis" International Journal of Scientific Research and Engineering Development, V9(6): Page(138-142) November - December 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.