IoT-Based Smart Soil Regeneration Tracking System Using ESP32 with AI-Based Soil Nutrient Prediction, Future Crop Recommendation & Automated Soil Rejuvenation Control

Abstract

Soil health plays a critical role in agricultural productivity, environmental sustainability, and crop quality. This paper presents an IoT-based smart soil regeneration tracking system using ESP32 with AI-based soil nutrient prediction, future crop recommendation, and automated soil rejuvenation control. The proposed system integrates soil moisture, TDS, and DHT11 sensors to continuously monitor soil and environmental parameters in real time. Sensor data is transmitted through IoT technology to the Blynk platform and Flask application for remote monitoring and analysis. Random Forest and Long Short-Term Memory (LSTM) algorithms are utilized for soil nutrient prediction, crop recommendation, and future soil condition forecasting. Based on analytical results, the system automatically controls irrigation pumps and valves to maintain suitable soil conditions. The developed system improves water management, reduces manual monitoring, enhances soil fertility assessment, and supports sustainable smart farming practices with efficient resource utilization and accurate decision-making. The architecture provides reliable real-time monitoring capabilities and enables intelligent agricultural automation for improving long-term soil productivity efficiently.