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.

Plant phenotyping relevance

植物工場でのクロロフィル蛍光画像取得、自動搬送、高スループット測定、特徴抽出、機械学習による生育予測を一体化した診断システムの開発が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The article describes CF imaging of lettuce seedlings and neural-network growth prediction, but contains no data availability statement, no public dataset/image deposit, and no code or model release with an authors' public URL. Only the article DOI itself is present.

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