S08 - Session P5 - Determination of optimal light spectrum for lettuce production in vertical farms using mixture design

Authors: Jang Se-hun *, Oh Myung-Min

In vertical farms, creating a proper light environment is crucial to improve crop yield due to the absence of natural light. Many studies related to light spectrum have been conducted, but most results were not enough to determine the optimal light quality. Thus, this study aimed to apply the mixture design as a tool to identify an optimum light spectrum and verify the light condition. Two-week-old seedlings were cultivated under normal growth conditions in a closed-type environmental control room for 4 weeks. Photosynthetic photon flux density of all treatments was set at 150 µmol·m -2 ·s -1 and red leaf lettuce was irradiated with 10 combinations of red (R), green (G), and blue (B) LEDs based on the axial mixture design; monochromatic R, G, and B and various combinations of R, G, or B (R:G=1:1, G:B=1:1, R:B=1:1, R:G:B=1:1:1, R:G:B=4:1:1, R:G:B=1:4:1, R:G:B=1:1:4). In the verification study, lettuce plants were cultivated under R, G, B, and various combinations of RGB (R:G=7:3, R:G=3:2, R:G:B=1:1:1, R:G:B=4:1:1, R:G:B=1:4:1, R:G:B=1:1:4). In the first study, the shoot fresh weight was the highest in R and increased with increasing red LED ratio. A similar trend appeared in the leaf area. In the verification study, the shoot fresh weight was the highest in R, followed by R:G=7:3, which was the same trend as the first study. Moreover, the leaf area was the highest in R and R:G=7:3. However, the marketability of red leaf lettuce was poor because of insufficient anthocyanin content in both treatments. Our results of mixture design predicted that 100% R was optimal light quality in terms of shoot growth of red lettuce and verified it. In this study, we suggested the potential of mixture design for creating the light spectrum to increase yield at harvest efficiently, but further studies are required to produce commercially available lettuce. This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) and Korea Smart Farm R&D Foundation (KosFarm) through Smart Farm Innovation Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Ministry of Science and ICT (MSIT), Rural Development Administration (RDA) (421033-4).