Optimization of LED Lighting Strategies using Multi-Spectral Imaging for Enhanced Crop Growth in Vertical Farming Systems

¹ Department of computers Techniques engineering, College of technical engineering, The Islamic University of Najaf, Iraq The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq The Islamic University of Babylon, Babylon, Iraq
² College of MLT, Ahl Al Bayt University, Karbala, Iraq
³ Department of CSE, GRIET, Hyderabad, Telangana, India

^* Corresponding author: mhussien074@gmail.com

Abstract

LED lighting strategies need to be properly designed to maximize crop growth and yield in vertical farming systems using controlled environments. To realize this, a system incorporating a combination of such advanced technologies like closed-loop feedback control, multi spectral imaging, MQTT (Message Queuing Telemetry Transport), BH1750 light intensity sensors, and Support Vector Machines (SVMs) is provided. Also, the closed loop feedback control system provides dynamic control of LED lighting based on real-time information fed from different sensors to guarantee a better condition of light as plant growth changes throughout time. Therefore, this system provides sophisticated control of lighting parameters that respond to environmental changes and plant responses. Many LED manufacturers use multi-spectral imaging to fine-tune the light spectrum their LEDs emit to plant's particular need at various stages of growth. Therefore, this calibration increases the photosynthesis efficacy and increases the plant growth to make the overall crop health better. Make sure that the data flows efficiently and reliably and that the central control system adjusts in real time through the help of MQTT, which enables data transmission specifically between sensors and lighting controllers. Light intensity sensors (BH1750) are important in this role as light levels are crucial to achieving optimal lighting throughout the growth cycle. SVMs are employed to analyze historical (or real-time) complex datasets to make lighting strategy predictions and to optimize them. Machine learning in this area helps contemporary decision-making be more informed, and, thus helps in achieving more effective and efficient adjustment and improvements in lighting efficiency. These integrated technologies are implemented and are forming a major step forward resulting in an estimated 15 percent improvement in crop yield and 20 percent reduction in energy consumed in lighting. Not only does this refine the accuracy and the efficacy of controlling agriculture, but it also fosters the growing of controlled environment agriculture in a more environmentally sound way. Horizontal approaches to Farming use these innovative solutions to increase productivity and efficiency as they help create more stable and more efficient agricultural processes.