Smart Agriculture Analytics Using Machine Learning and Cloud Storage

Abstract

The convergence of machine learning (ML) and cloud-based data management has opened new frontiers in precision agriculture. This paper presents a comprehensive smart agriculture analytics system that leverages a Decision Tree Regressor model to predict crop yield based on environmental and soil parameters, including soil pH, temperature, humidity, wind speed, and macro-nutrients such as nitrogen (N), phosphorus (P), and potassium (K). The system is architected as a Flask-based web application with a Firebase Realtime Database backend for secure, scalable cloud storage of user data and prediction records. Beyond yield forecasting, the platform incorporates an intelligent advisory module that benchmarks input parameters against crop-specific optimal ranges, generating actionable cultivation recommendations. A seasonal suitability analysis engine further assists farmers in aligning crop selection with prevailing agro-climatic cycles. The system incorporates user authentication, multilingual interfaces supporting English, Hindi, and Telugu, and a historical analytics dashboard for trend monitoring. Experimental evaluations demonstrate reliable prediction accuracy with negligible latency, confirming the system's viability as a practical decision-support tool. The architecture's modularity facilitates future integration of IoT sensor streams, deep learning models, and satellite imagery analytics, positioning it as a scalable foundation for next-generation precision farming platforms