Greenhouse-based vegetable high-throughput phenotyping platform and trait evaluation for large-scale lettuces

Abstract

Large-scale screening and assessment of lettuce resources are valuable to help discover significant traits and assist in genetic breeding. In this research, a greenhouse-based vegetable high-throughput phenotyping platform (VHPP) was established to evaluate the multidimensional characteristics of various lettuce varieties. The platform contains an imaging unit with four degrees of freedom (DOFs) to cruise on the crop overhead for image acquisition. The platform also has an automated global semantic phenotyping pipeline (GSPP) for locating pots in sequential images and matching each plant at different growth points. Multidimensional image-based traits were automatically extracted and classified into six categories, including geometry, structure, texture, color, color moment and color indices. We calculated and evaluated 63 static traits (ST) and 189 dynamic traits (DT) by principal component (PC), correlation and heritability analysis, and new PCs provided valuable perspective in describing the lettuce canopy. The results demonstrated the ability of a phenotyping system and pipeline to rapidly investigate and evaluate the growth status of thousands of vegetables. Besides, we identified lots of valuable traits that could be of positive significance in revealing the genetic basis of complex features and exploring the excellent attributes for use in the large-scale lettuce screening and assessment.

Plant phenotyping relevance

レタスの画像取得プラットフォームと自動形質抽出パイプラインを開発・適用し、多数の形態・構造・色などの形質を評価しているため、植物フェノタイピング手法が研究の中心である。

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