Computational Modeling and Optimization Applied to Controlled Environment Agriculture Lighting Systems
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Supplemental lighting is integral to year-round production of greenhouse crops; however, the location of lights within the greenhouse and its effects on lighting uniformity in the growing space is often not considered. This research was conducted to assist Controlled Environment Agriculture (CEA) producers and researchers in identifying the optimum lighting layout for improved lighting uniformity. The methodology outlines the development of an algorithm for modelling supplemental lighting, based on standardized goniophotometric data, and optimizing the location of lighting fixtures within the CEA environment. This resulted in the production of a software package in the Python Programming Language that could model and optimize lighting uniformity for unique CEA environments based on their physical dimensions and specified lighting fixture. Through the implementation of this novel software, the lighting uniformity for hypothetical CEA environments with a small number of supplemental lighting fixtures were optimized.