Smart Irrigation System with IoT, Machine Learning, and Solar Power for Efficient Plant Care - Justin M. A. Capcha-Ochoa; Jefferson A. Chahua-Benito; Miguel A. Serafin-Cayllahua; Sebastian E. Mamani-Martinez; Jesus G. Mendivil-Imbertis; Jordan I. Mendoza-Fernandez; Roberto J. M. Casas-Miranda; Maritza Cabana-Cáceres; Cristian Castro-Vargas (Emerging Science Journal)
Efficient irrigation in green areas and homes is essential for environmental sustainability and water conservation. This study aims to develop an intelligent irrigation system based on the Internet of Things (IoT) and machine learning to optimize water use, improve plant monitoring, and enhance security. Two ESP32 microcontrollers and an ESP32-CAM were deployed to manage humidity, temperature, light sensors, and irrigation automation using a solenoid valve. A modified Yolov3-tiny model detects signs of dehydration and chlorosis in plants, while facial recognition restricts access to authorized users. Data is processed through IoT platforms such as Adafruit IO and Telegram, ensuring continuous solar-powered monitoring. Furthermore, integration with YouTube Live and Dropbox enables remote monitoring and intrusion detection. Experimental results indicate a 43.7% reduction in water consumption, efficient detection of plant problems (93.86% accuracy), and increased security. Based on AI and renewable energy, this innovative approach surpasses traditional systems and represents a scalable and sustainable solution for innovative irrigation management.