Authors: Mohammed Munyim Hussain V, Poorvaj K P, Prashanth S R, Preetham M, Mr P Prasanna
Abstract: Agriculture remains a cornerstone of economic activity across developing nations, yet smallholder farmers routinely face yield gaps caused by uninformed decisions on crop selection, soil nutrition, and disease management. This paper presents AGRI HUB, a web-based Crop and Soil Management System that unifies several machine-learning and deep-learning services behind a single Flask-driven interface. Four core modules are delivered: (i) smart crop recommendation using a Random Forest classifier trained on seven agro-climatic parameters, achieving 99.55% accuracy across 22 crop classes; (ii) soil nutrient analysis and fertilizer recommendation through NPK deficit computation against crop-specific thresholds; (iii) plant disease detection using a ResNet-9 convolutional neural network capable of classifying 38 disease categories from leaf photographs; and (iv) real-time, weather-driven activity planning by consuming OpenWeatherMap API data to generate seven-day farming calendars. An AI chatbot powered by the Google Gemini large language model supplements the analytical modules with conversational agronomic guidance. A crop profitability comparison dashboard rounds out the system, enabling evidence-based economic decisions. Experimental evaluation confirms that the integrated platform consistently outperforms single-module alternatives in both accuracy and decision breadth, offering a scalable, cost-effective tool for precision agriculture.
