Smart Soil Sense: An IoT-Based Intelligent Crop Recommendation System Using Machine Learning for Precision Agriculture

Abstract: Smart agricultural monitoring plays a crucial role in improving crop yield and maintaining soil health. In India, many farmers face challenges in selecting suitable crops and managing soil nutrients, often leading to excessive fertilizer usage and financial loss. This paper presents Smart Soil Sense, an Internet of Things (IoT) based system that analyzes soil nutrients including Nitrogen (N), Phosphorus (P), and Potassium (K) along with environmental factors such as temperature, humidity, pH, and rainfall to recommend appropriate crops. The system integrates real-time sensor data collection using ESP32 microcontroller, NPK sensors, and DHT11/DHT22 environmental sensors. Multiple machine learning algorithms were evaluated, including Decision Tree, Naive Bayes, Support Vector Machine, Logistic Regression, Random Forest, and XGBoost. Experimental results indicate that XGBoost achieves the highest accuracy of 99.31%, demonstrating its effectiveness for crop prediction. The proposed system also incorporates a disease detection module using convolutional neural networks (CNN) for image classification to identify plant leaf diseases and recommend appropriate pesticides. Overall, the approach offers a cost-effective and reliable solution for improving agricultural productivity and supporting data-driven decision-making in farming practices, with potential to reduce fertilizer misuse by up to 30-35% and improve crop yields through precision agriculture.

Keywords: Smart Agriculture, Internet of Things (IoT), Machine Learning, Soil Analysis, XGBoost, Crop Recommendation, Precision Farming, NPK Sensors, ESP32.