Agriculture has long been the backbone of the Indian economy, yet farmers, particularly smallholders, continue to struggle with accessing timely, reliable, and actionable information about crop health, weather patterns, and commodity market prices. This paper presents AgriChatbot, a full-stack conversational system built on the MERN (MongoDB, Express.js, React, Node.js) architecture with a Python-based backend, developed to bring integrated agricultural advisory services to farmers through a single chat interface. The system brings together a natural language processing engine with three dedicated modules: an AI powered crop disease detector that works from leaf images, a real-time weather forecasting component, and a live agricultural market price retrieval service. Users interact through a responsive web application that routes their queries to the right backend service and returns structured, plain language responses. The architecture keeps the React frontend, Node.js/Express middleware, and specialist Python services clearly separated, making each module straightforward to develop and scale on its own. AgriChatbot is, at its heart, an attempt to make agricultural expertise more democratically accessible, bringing together several streams of specialized advice into one conversational platform that any farmer with a smartphone can use.
Ali Ashjaa, S. Ameer Ali RIzvi, Abdus Samad , Mohammad Suaib (Wed,) studied this question.