Optimization of Aquaponic Lettuce Evapotranspiration Based on Artificial Photosynthetic Light Properties Using Hybrid Genetic Programming and Moth Flame Optimizer

Land and water resources, climate change, disaster risks significantly affect the agricultural sector. An effective solution for growing crops to improve productivity optimize use of resources is through controlled-environment agriculture (CEA). Evapotranspiration (ET) an important greenhouse crop attribute that can be optimized optimum plant growth. Light intensity radiation are significant controlling ET. To address this challenge, study successfully determined properties artificial light minimum evapotranspiration rate head development-stage harvest-stage lettuce under light-period dark-period using genetic programming bio-inspired algorithms namely, grey wolf optimization (GWO), whale algorithm (WOA), dragonfly (DA), moth flame (MFO). MFO provided global configured models. Results showed requires higher with lower visible infrared ratio (Vis/IR) than when exposed light. On other hand, Vis/IR respiration reaction. Findings utilized in improving yield agriculture.