Multi-layered modeling of photosynthetic efficiency under spectral light regimes in AI-optimized indoor agronomic systems
Adeoluwa Abraham Olasehinde, Anthony Osi Blessing, Adedeji Adebola Adelagun, Somadina Obiora
Abstract
The integration of artificial intelligence (AI) with plant physiological modeling offers transformative opportunities in precision indoor agriculture, where environmental variables can be tightly controlled to maximize crop productivity and resource efficiency. Among these variables, light quality—particularly spectral composition—plays a critical role in regulating photosynthetic efficiency, morphogenesis, and yield outcomes. This paper explores a multi-layered modeling approach to photosynthetic optimization under varying spectral light regimes using AI-driven control systems in indoor agronomic environments. The study begins by examining the physiological mechanisms through which plants respond to red, blue, far-red, and green wavelengths, emphasizing chlorophyll absorption dynamics, photoreceptor signaling, and stomatal conductance. These biological insights inform the construction of computational models that predict photosynthetic rates and biomass accumulation across different lighting scenarios. The second layer integrates machine learning algorithms—such as deep neural networks and reinforcement learning—to process real-time sensor data on photosynthesis, CO₂ assimilation, and plant canopy reflectance, enabling dynamic light adjustment for each growth stage. AI models are further trained to identify genotype-specific light responses, allowing the customization of lighting schedules for diverse crop varieties. Case studies demonstrate significant improvements in light-use efficiency, energy conservation, and crop quality when spectral lighting is optimized using AI algorithms. Ethical and operational considerations related to data governance and hardware-software integration are also addressed. By combining physiological understanding with AI capabilities, this multi-layered framework supports more adaptive, resource-efficient, and sustainable approaches to indoor crop production. The findings advance both agronomic performance and system intelligence, paving the way for next-generation indoor farming solutions.