title: High-Throughput Growth Prediction for Lactuca sativa L. Seedlings Using Chlorophyll Fluorescence in a Plant Factory with Artificial Lighting.
abstract: Poorly grown plants that result from differences in individuals lead to large profit losses for plant factories that use large electric power sources for cultivation. Thus, identifying and culling the low-grade plants at an early stage, using so-called seedlings diagnosis technology, plays an important role in avoiding large losses in plant factories. In this study, we developed a high-throughput diagnosis system using the measurement of chlorophyll fluorescence (CF) in a commercial large-scale plant factory, which produces about 5000 lettuce plants every day. At an early stage (6 days after sowing), a CF image of 7200 seedlings was captured every 4 h on the final greening day by a high-sensitivity CCD camera and an automatic transferring machine, and biological indices were extracted. Using machine learning, plant growth can be predicted with a high degree of accuracy based on biological indices including leaf size, amount of CF, and circadian rhythms in CF. Growth prediction was improved by addition of temporal information on CF. The present data also provide new insights into the relationships between growth and temporal information regulated by the inherent biological clock.
authors.affiliation: Department of Mechanical Engineering, Graduate School of Engineering, Osaka Prefecture University Osaka, Japan.
authors.forename: Shogo
authors.initials: S
authors.lastname: Moriyuki
authors.affiliation: Department of Mechanical Engineering, Graduate School of Engineering, Osaka Prefecture UniversityOsaka, Japan; Japan Science and Technology Agency, PRESTOSaitama, Japan.
authors.forename: Hirokazu
authors.initials: H
authors.lastname: Fukuda
keywords: chlorophyll fluorescence; circadian clock; diagnosis system; imaging; lettuce; machine learning