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Multi-layered modeling of photosynthetic efficiency under spectral light regimes in AI-optimized indoor agronomic systems
1 Department of Biochemistry, University of Ibadan, Ibadan, Nigeria.
2 Department: Crop Science, Faculty of Agriculture, University of Benin, Nigeria.
3 Department of Agricultural Biochemistry and Nutrition unit, University of Ibadan.
4 Department of Biochemistry and Nutrition, Nigerian Institute of Medical Research, Nigeria.
Research Article
International Journal of Science and Research Archive, 2022, 06(01), 367-385.
Article DOI: 10.30574/ijsra.2022.6.1.0267
Publication history:
Received on 26 May 2022; revised on 24 June 2022; accepted on 27 June 2022
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.
Keywords:
Photosynthetic efficiency; Spectral light optimization; Artificial intelligence in agriculture; Indoor agronomic systems; Precision plant physiology; Machine learning for crop modelling
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
