AI-Based Intelligent Crop Selection Using Market and Environmental Analysis

Agriculture is essential in economic growth and food availability, particularly in India, where a significant percentage of the population depends on farming for their livelihoods. However, agricultural communities encounter various difficulties, such as climate variability, irregular rainfall, soil degradation, water scarcity, and fluctuating market prices. Traditional crop selection methods often depend on traditional knowledge and traditional farming methods, which may may not sufficiently consider variations in environmental factors and market demands. To overcome these limitations, this study introduces an Artificial Intelligence (AI)-based intelligent crop selection system that combines environmental parameters and economic and market indicators to improve farming decisions. The developed framework examines important environmental variables, including soil nutrient content, temperature, humidity, rainfall, and soil pH, together with financial indicators such as market demand for crops, market price, and expected economic returns. ML-based predictive techniques, including Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Networks (ANN), are developed and assessed for crop prediction and recommendation. The system is designed to determine appropriate crop varieties by considering environmental compatibility and economic potential, providing farmers with recommendations based on analytical data rather than relying solely on conventional crop selection techniques. The experimental evaluation compares the predictive performance of the implemented models and identifies an appropriate model for crop recommendation. By integrating environmental suitability with market analysis, the proposed approach seeks to improve crop selection, optimize agricultural resource use, reduce cultivation risks, and improve farmers' financial returns for farmers. The system can also be extended through Internet of Things (IoT) technologies and cloud computing to enable real-time environmental monitoring and scalable agricultural applications. This approach contributes to precision agriculture by combining machine learning-based prediction with environmental and economic analysis to promote sustainable farming practices and informed agricultural decision-making.