IoT-Driven Smart Embedded System for Efficient Quality Food Production in Controlled Environments

Latin American agriculture encounters significant challenges such as inefficient land use and vulnerability to climate change, with smallholder farmers being particularly affected. This paper proposes a modular Internet of Things (IoT)-based system that integrates embedded systems, environmental sensors, and Artificial Intelligence (AI) for real-time monitoring and adaptive control. The system utilizes a Raspberry Pi coupled with advanced sensors measuring temperature, humidity, potential of hydrogen (pH), liquid level, light intensity, air quality, and electrical current. Actuators regulate nutrient delivery in response to pH levels, while intelligent lighting systems enhance plant growth conditions. Remote monitoring facilitated by a vision-equipped camera system allows the detection of anomalies and the tracking of plant growth. Experimental results demonstrated temperature stability of ±1∘C, humidity variation within ±5%, 92% accuracy in anomaly detection, and response times of less than 10 seconds.