AgroSphere: A Terra-to-Trade Intelligent Decision Support Framework for Precision Agriculture Using Machine Learning and Real-Time Analytics

This paper presented AgroSphere: A Terra-to-Trade Intelligent Decision Support Framework for Precision Agriculture Using Machine Learning and Real-Time Analytics, an integrated web-based platform developed to support farmers throughout the agricultural lifecycle. The proposed framework addresses key challenges in modern agriculture, including inappropriate crop selection, weather uncertainty, fluctuating market prices, inefficient financial planning, and limited access to crop health assessment. The framework successfully integrates five major modules: Crop Recommendation, Weather Intelligence, Market Intelligence, Cost–Profit Prediction, and Crop Hygiene Assessment. By leveraging the Random Forest machine learning algorithm, real-time weather services, and agricultural market data, AgroSphere provides farmers with accurate recommendations and actionable insights for effective decision-making. The implementation of a FastAPI-based backend and a responsive web interface ensures scalability, accessibility, and ease of use. The integrated architecture eliminates the need for multiple independent agricultural applications by consolidating essential farming services into a single platform. This enables farmers to optimize resource utilization, improve productivity, reduce cultivation risks, and make economically informed decisions. Overall, AgroSphere demonstrates the potential of combining machine learning, real-time analytics, and modern web technologies to support sustainable precision agriculture. The proposed framework contributes toward the digital transformation of agriculture by providing an intelligent, affordable, and scalable decision-support system that can benefit farmers, agricultural organizations, and policymakers alike.

[1] H. H. Sarma, B. C. Das, T. Deka, S. Rahman, M. Medhi, and M. Kakoti, "Data-driven Agriculture: Software Innovations for Enhanced Soil Health, Crop Nutrients, Disease Detection, Weather Forecasting and Fertilizer Optimization in Agriculture," Journal of Advances in Biology & Biotechnology, vol. 27, no. 5, 2024. [2] M. Jaswal et al., "Smart Agriculture: Combining Crop Recommendation, Yield Prediction and Environmental Analysis," in Proceedings of the IEEE EmergIN Conference, 2024. [3] S. A. Kotkar, I. S. Narkhede, R. R. Shirsath, and R. Naik, "Crop Price Prediction using Machine Learning," International Journal of Data Science. [4] S. Tripathi and A. Pandey, "AI-based Chatbot for Smart Agriculture," International Journal of Advanced Computer Science and Applications. [5] J. R. Quinlan, "Induction of Decision Trees," Machine Learning, vol. 1, no. 1, pp. 81–106, 1986. [6] L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [7] OpenWeather, "Weather API Documentation." Available: [8] AGMARKNET, "Agricultural Marketing Information Network." Available: [9] Scikit-learn Developers, "Random Forest Classifier Documentation." Available: [10] Food and Agriculture Organization (FAO), "The State of Food and Agriculture," FAO, Rome.