AI-Based Soil Fertility Monitoring Using Sensor Data and Machine Learning

Abstract

The declining soil fertility due to intensive agricultural practices and imbalanced nutrient management has necessitated the development of advanced monitoring systems for sustainable crop production. This study presents an artificial intelligence-based soil fertility monitoring framework that integrates sensor data with machine learning techniques to enable accurate and real-time assessment of soil health. The proposed methodology utilizes data collected from in-field soil sensors, including parameters such as nitrogen, phosphorus, potassium, pH, moisture content, and temperature, along with historical soil records. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure consistency and reliability. Advanced machine learning models, including Random Forest, Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, are employed to capture complex nonlinear relationships between soil properties and fertility status. The system is designed with real-time data acquisition and adaptive learning capabilities, enabling continuous monitoring and dynamic analysis under changing field conditions. The models are trained and validated using region-specific datasets to ensure robustness across diverse soil types and agro-climatic environments. Experimental results demonstrate that the proposed approach achieves high prediction accuracy and significantly improves soil fertility assessment compared to conventional laboratory-based methods. Additionally, the system provides actionable insights for optimized fertilizer application and nutrient management, reducing input costs and minimizing environmental impact. The findings highlight the effectiveness of combining artificial intelligence with sensor-based monitoring for precision agriculture applications. The study concludes that AI-based soil fertility monitoring systems offer a scalable, efficient, and data-driven solution for enhancing soil health, improving crop productivity, and promoting sustainable agricultural practices.