Crop Monitoring System Using IoT, Solar Energy and Decision Tree Algorithm

Peru's diverse topographical regions offer optimal conditions for agriculture, but a lack of technology hinders efficiency, leading to food imports despite the country's potential. This paper aims to design an Internet of Things-based monitoring system where the specific objectives are focused on building a solar-powered power stage and integrating machine learning algorithms to help determine crop health. The development methodology includes the evaluation of the use of sensors to measure environmental and soil temperature and humidity, precipitation and hydrogen potential to help identify the health status of crops using machine learning algorithms (decision trees) and transmit the information to a Blynk real-time visualization server. The system components include a device based on an ESP32 module operating in low-power mode, a solar power stage, a data management stage with Blynk with Wi-Fi communication. The results show that the IoT device was adapted for outdoor environments protected by an IP65 housing and can operate for approximately 12 days with a 3000 mAh battery. The main result is that the Random Forest model stands out for having a 98% accuracy when inferring the state of crop conditions. Future improvements can include more efficient solar cells to improve the system's charging conditions. Doi: 10.28991/ESJ-2025-09-02-06 Full Text: PDF