Design of soil nutrients analysis system and crop prediction model using IOT and ensemble model

Abstract

This research explores the integration of Internet of Things (IoT) technology with machine learning ensemble models to create a comprehensive soil nutrient analysis system for precision agriculture. The study proposes a novel framework that combines real-time soil sensing capabilities with advanced predictive analytics to optimize crop yield prediction and nutrient management. The system incorporates multiple soil sensors to collect data on essential nutrients (N, P, K), pH, moisture content, and additional environmental parameters. An ensemble machine learning approach integrating Random Forest, Gradient Boosting, and Support Vector Machine algorithms was employed to enhance prediction accuracy. Experimental results demonstrate that the proposed system achieves 94.2% accuracy in crop yield prediction and 91.7% accuracy in identifying optimal nutrient requirements, outperforming single-model approaches. The system's IoT architecture enables real-time data collection, storage, and analysis through a cloud platform, providing farmers with actionable insights via a user-friendly mobile application. This research contributes to sustainable agriculture practices by enabling precise nutrient management, reducing fertilizer waste, and optimizing crop production based on site-specific conditions.