GPS AND ML BASED SOIL PROPERTY EXTRACTION AND CROP RECOMMENDATION SYSTEM

This paper presented a GPS and MLbased soil property extraction and crop recommendation system for precision agriculture. The proposed framework integrates geospatial data with soil parameters to provide accurate and locationspecific crop recommendations.   Future work includes integrating IoTbased soil sensors for realtime monitoring, applying deep learning models for improved predictive accuracy, incorporating satellite imagery for soil health assessment, and implementing mobilebased applications for farmer accessibility.  

[1] Y. Chen et al., “Machine Learning Based Soil Nutrient Analysis for Precision Agriculture,” IEEE Access, 2023.   [2] M. Kumar et al., “Ensemble Learning Approaches for Crop Recommendation Systems,” IEEE Transactions on Artificial Intelligence, 2024.   [3] L. Zhang et al., “Geospatial Data Integration for Smart Farming Applications,” IEEE Internet of Things Journal, 2024.   [4] R. Patel et al., “Hybrid Environmental and SoilBased Crop Prediction Models,” IEEE Access, 2025.   [5] A. Singh et al., “Edge Based Precision Agriculture Using Machine Learning,” IEEE Internet of Things Journal, 2026.   [6] T. Nguyen et al., “Feature Engineering Techniques for Agricultural Data Analytics,” IEEE Access, 2023.   [7] P. Wang et al., “Climate Aware Crop Recommendation Using Machine Learning,” IEEE Transactions on Artificial Intelligence, 2024.   [8] K. Roy et al., “Big Data Analytics for Sustainable Agriculture,” IEEE Access, 2025.   [9] J. Brown et al., “RealTime Agricultural Monitoring Systems,” IEEE Access, 2026.   [10] S. Verma et al., “Soil Health Assessment Using Intelligent Predictive Models,” IEEE Access, 2024.   [11] D. Sharma et al., “Scalable Agricultural Decision Support Systems Using AI,” IEEE Transactions on Artificial Intelligence, 2025.