Smart Soil Analysis & Crop Recommendation System
Author : T Rajeshwari and P Halima Farhana
Abstract :
Agriculture plays a critical role in ensuring food security, yet farmers often face challenges in selecting the most suitable crops for their soil and climate. This research presents a Smart Soil Analysis & Crop Recommendation System that leverages sensor-based soil data and machine learning algorithms to provide accurate crop suggestions. The system measures key soil parameters such as pH, moisture, nitrogen, phosphorus, and potassium content, and integrates environmental factors like temperature and rainfall. Based on these inputs, predictive models analyze soil fertility and recommend optimal crops to maximize yield and sustainability. Experimental results demonstrate that the system can increase productivity and reduce resource wastage compared to traditional practices. This intelligent approach also empowers small-scale farmers by providing actionable insights in a user-friendly manner. The study concludes that combining IoT-enabled sensors with data-driven models enhances precision agriculture and contributes to sustainable farming practices.
Keywords :
Soil Analysis, Crop Recommendation, IoT Sensors, Machine Learning, Precision Agriculture.
