Smart Irrigation System For Rice Farming
Description
This dataset can be used to present several key innovations in the development of an AI-IoT-based Smart Drip Irrigation System (SDIS) specifically designed for rice plants by considering local agronomic characteristics. First, this system integrates four key input parameters to ensure precision and adaptability in irrigation management: (1) rice plant growth phase, which determines the adjustment of the dose, irrigation frequency; (2) soil moisture, as the main indicator to activate irrigation only when the soil water content is below the optimal threshold; (3) Light Intensity; (4) air temperature and (5) and air humidity (measured using a DHT22 sensor), which plays a role in mitigating plant stress due to extreme microclimate conditions.
Files
Steps to reproduce
dataset was taken on rice farming, using ESP32 as the main microcontroller that functions as the system control center connected to thingspeaks as a data storage. ESP32 receives input from various sensors including soil moisture sensor (YL9) to measure water content in the soil, RTC (Real-Time Clock) module to determine plant growth phase based on time, In addition, this system is also equipped with DHT21 sensor to monitor humidity and air temperature, and BH1750 sensor which plays an important role in measuring light intensity in agricultural environments. based on sensor data is then used to carry out ON/OFF commands in the process of watering rice plants
Institutions
- Universitas Negeri Malang