SMART FARMING REVOLUTION: IOT-DRIVEN SOIL ANALYSIS AND AI-POWERED CROP RECOMMENDATION MODELS

Abstract

Traditional soil testing methods fail to provide real-time insights, leading to suboptimal fertilization strategies and crop selection decisions. This study presents an IoT-enabled precision agriculture system integrating real-time soil nutrient monitoring with deep learning-based crop recommendation. We deployed electrochemical and spectroscopic sensors across 50 farms in three climate zones, collecting 15,000 soil samples over 24 months (2022-2024). Our deep learning model achieved 93% classification accuracy (F1-score: 0.91), significantly outperforming rule-based (85%), machine learning (90%), and hybrid models (89%) (p<0.001). The system demonstrated 15-20% yield improvements across wheat, corn, soybean, and barley, with corn showing the highest response (20% increase) to optimized nutrient management (N:60, P:30, K:25 ppm). Economic analysis revealed system costs of $150-200 per sensor with ROI achievable within 1.5 growing seasons. Seasonal analysis identified spring as the critical period for nitrogen and potassium management, while winter showed elevated phosphorus levels. This research demonstrates that IoT-integrated deep learning systems can significantly enhance agricultural productivity while reducing input costs by 18%, contributing to sustainable and economically viable precision farming.