Application of Partial Least Squares (PLS) Regression to Control Light Source for Solanum lycopersicum Seedling Growth in Plant Factory with Artificial Lighting

抄録

The use of LEDs as a light source in plant factories with artificial lighting is expected to reduce costs, but it has been reported that some crops do not grow as expected due to differences in the wavelength of the light source. Therefore, it is necessary to design LEDs with the appropriate wavelength for each crop to be grown. On the other hand, there is research toward the realization of smart plant factories that utilize artificial intelligence, and artificial intelligence may contribute to the design of the light environment in plant factories. In this study, we selected mini-tomatoes as a model crop, and prepared fluorescent lamps and LEDs as the light environment during seedling growth, respectively, and searched for suitable light source wavelengths while investigating the relationship with growth conditions using statistical analysis methods, one of the artificial intelligence techniques. We investigated the relationship between multiple light environments, PPFD, R/B ratio, and spectrum of wavelengths respectively, using LEDs, fluorescent lamps, and dimming filters, and growth indices stem diameter in order to clarify the light environments that contribute to growth. The correlation between the measured light environment and crop growth results was objectively shown by PLS regression analysis, and the contributing wavelengths were explored by calculating the selectivity ratio and regression coefficient. As a result, it was suggested that stem diameter was promoted at around 550 nm and 630 nm, and suppressed at around 460 nm.