A Conceptual Framework for Soil Classification and Crop Recommendation Using Machine Learning and Satellite-Sensor Data Integration

This paper introduces a conceptual framework for integrating machine learning techniques in soil classification and crop recommendation, aiming to enhance precision agriculture practices. The proposed system comprises two key stages: in the first stage, soil attributes such as pH, moisture content, temperature and various other factors are analyzed using the Random Forest algorithm to classify soil types. In the second stage, these classifications are fed into a Gradient Boosting Machine (GBM) to provide optimal crop recommendations based on the integrated dataset. This conceptual framework leverages the synergy of multiple data sources to enhance agricultural decision-making by offering tailored recommendations suited to specific soil types. Unlike conventional methods, which often treat soil classification and crop recommendation independently, this integrated approach aims to provide more accurate and context-specific recommendations. Although this paper primarily outlines the theoretical framework and methodologies, the ongoing development and implementation will demonstrate its real-world applicability in future research. The potential for this system to contribute to sustainable agriculture, improved crop yield, and resource efficiency is significant, making it a valuable tool for modernizing agricultural practices.