Hybrid Genetic Programming and Multiverse-based Optimization of Pre-Harvest Growth Factors of Aquaponic Lettuce Based on Chlorophyll Concentration
Optimizing photosynthesis is vital in maintaining quality farm produce in the agricultural food production sector. The nonlinear behavior of the interaction of crop pre-harvest growth factors can promote or retard its growth. This study employed multigene symbolic regression genetic programming (MSRGP) in developing the chlorophyll-a fitness function allied with bioinspired algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and multiverse optimization (MVO), in determining the ideal combination of carbon dioxide, light intensity, air temperature, and humidity that will induce photosynthesis based on aquaponic lettuce (Lactuca sativa var. Altima) leaf chlorophyll-a concentration. Light spectra were characterized through the floating leaf disk technique, which resulted in white spectra as the most photosynthetic conducive based on the light reaction and dark respiration. Leaf spectro-textural-morphological signatures were extracted for non-destructive MSRGP chlorophyll-a concentration measurement. Carbon dioxide and humidity have a strong positive impact on chlorophyll-a concentration. Photosynthesis is impeded by Vis/IR above 7.817. The hybrid MSRGP-MVO generated the ideal global solution of 880.744 ppm of CO2, 543.147 μmol m-2 s-1 of the visible white light spectrum, 22.238 °C air temperature, and 67.742% humidity which resulted in 651.144 mg g-1 of Chl-a, 0.934 leaf weight ratio, 0.066 roots to shoot ratio, 141 xylem vessels mm-2 127.389 stomata mm-2, and more prominent intracellular chloroplast concentration for the harvest stage lettuce. The established standard for fresh weight, chlorophylls a and b, and vitamin C concentrations are essential for developing an adaptive nutrient management system to maintain the expected growth signatures of lettuce at the end of the 6-week cultivation cycle.
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