Integrated ML-Calibrated Sensing with Neural Network Control for Horticultural Lighting

This paper presents the design and experimental validation of a modular intelligent platform for energy-efficient lighting and monitoring in controlled environment agriculture (CEA). The proposed architecture integrates dimmable LED lighting with neural network–based spectral optimization, ma- chine learning–based light intensity estimation, image-based plant monitoring, and IoT-enabled data acquisition. The plat- form emphasizes a unified control framework that bridges perception, control, and application layers, enabling adaptive real-time decision-making and modular scalability. The system was implemented and tested in a greenhouse environment, demonstrating a 28% reduction in energy consumption per gram of dry biomass while maintaining plant health and productivity. The results underscore the effectiveness of the proposed architecture in advancing intelligent control and automation strategies for sustainable horticultural systems.