TerraSense: IoT-Enabled Soil Analysis Framework for Real-Time Crop Recommendation

Abstract

Agriculture is the backbone of global sustainability, yet it faces challenges that demand smarter, data-driven solutions. The escalating complexities within modern agriculture, driven by climate volatility and the need for sustainable resource utilization, necessitate a decisive shift toward Precision Farming methodologies. This research presents TerraSense, an advanced, low-cost Internet of Things-enabled framework architected for the continuous, real-time analysis of critical soil and microclimatic parameters. Accurate soil analysis, encompassing pH, moisture content, and other conditions such as temperature and humidity, is fundamental to optimizing nutrient uptake and mitigating localized crop stress. TerraSense leverages a high-performance ESP32 microcontroller and a custom-calibrated sensor array to acquire data with high fidelity. This streaming dataset is then directed via USB serial communication to a host environment. A specialized Machine Learning classifier, utilizing the validated four-dimensional feature vector, instantly generates an actionable recommendation for the most viable crop. The core contribution of TerraSense is its ability to deliver immediate, hyper-localized intelligence via an onboard OLED dashboard. Experimental validation confirms a deterministic end-to-end latency of approximately 120 milliseconds, comprising sensor acquisition, signal conditioning, inference, and feedback synchronization, well within the requirements for real-time, internet-independent field deployment. This architecture effectively bridges the gap between complex agricultural science and practical, real-time decision-making, significantly enhancing resource efficiency and potential yield maximization in small-to-medium scale farming operations. The framework's detailed integration demonstrates a robust, scalable blueprint for future smart agriculture systems.