DEVELOPMENT OF INTEGRATED INDUSTRIAL GREENHOUSE PLANT PRODUCTION USING ESP32 MCU WITH MULTIPLE SENSOR MONITORING

Abstract

Agriculture is a critical sector for global economic stability and food security, making technological innovations vital for sustainable development. This study focuses on the development of an Integrated Industrial Greenhouse Plant Production system using the ESP32 microcontroller with multiple sensor monitoring. The system is designed for reliability, automation, and energy efficiency, incorporating solar panels as a renewable energy source to ensure continuous operation. Additional features include a HuskyLens AI camera for plant health monitoring, Blynk IoT for cloud interfacing, and an uninterrupted power supply for consistent performance in diverse conditions. The researchers adopted the Experimental Prototyping Method to design, build, and refine the functional prototype. This iterative process allowed for continuous feedback and modification to meet user needs. A purposive sampling technique was used to select 50 respondents from Quezon City for system evaluation. Data were collected through a structured survey rated on a 4-point Likert scale to assess the prototype's usability, functionality, and performance. The developed prototype was fully operational and successfully met the study’s objectives. The system functioned as intended in terms of automation, remote control, and multi-sensor monitoring. Respondent feedback indicated that the system performed efficiently and provided substantial improvements compared to traditional greenhouse setups. The integration of smart technologies enhanced overall productivity and environmental control. The implementation of the Experimental Prototyping Method enabled the successful development of a miniature industrial greenhouse, including a designated plant production zone and a control room. Key components included the Main Control System, multiple environmental sensors (e.g., CO₂ and soil moisture), a mist distribution mechanism, and a drip irrigation system. A 100-watt solar panel provided sustainable energy, ensuring uninterrupted operation. The HuskyLens AI camera enabled real-time plant health classification, distinguishing between healthy and unhealthy specimens. Findings confirmed that the system achieved reduced energy consumption and environmental impact while maintaining optimal growing conditions. This study demonstrates a viable model for integrating smart, energy-efficient technologies into greenhouse plant production.