Enhancing Smart Agriculture through Multi-Stage Feature Selection and IoT-Driven Soil Intelligence

Abstract

The combination of inefficient irrigation methods and inappropriate crop selection in semi-arid areas results in wasted water resources and decreased agricultural yields. Our proposed framework integrates Artificial Intelligence (AI) with Internet of Things (IoT) to optimize crop suitability assessment and irrigation planning through real-time soil analysis. The system uses IoT sensors to obtain dynamic environmental and soil data including pH levels and moisture and nutrient measurements before applying machine learning algorithms to generate actionable insights. The proposed TriFS method implements a three-stage feature selection process which combines PCA with PSO and Lasso Regression to improve classification performance and model understanding. The selected features from TriFS serve as input to train k-Nearest Neighbors (kNN) and XGBoost classifiers which achieved superior results in identifying suitable crops and irrigation plans. The experimental evaluation of a real-world dataset demonstrated that the combination of TriFS with XGBoost produced classification accuracy above 97% which surpassed the results of traditional single-stage selection approaches. The research develops an intelligent scalable decision-support framework for precision agriculture which optimizes resources through data-driven approaches to enhance sustainability.

Key Words: Artificial Intelligence, Internet of Things, Feature Selection, Smart Agriculture, Irrigation Optimization