Temporal Assessment of Drought Stress Progression through Large-Scale Machine- Learning-Based Phenotyping

Abstract

ORCiD: [0000-0001-6507-9985 of Jinyoung Y. Barnaby], [0000-0001-9082-6583 of Scott E. Warnke] Precise assessment of large mapping populations, comprising a few thousand plants including replications (a prerequisite step for breeding) is time-consuming and labor-intensive. Furthermore, phenotyping results tend to be variable and subjective depending on who is doing the scoring. One way to overcome these limitations is by collecting more data in the form of digital images, and precisely evaluating phenotypic variation in stress severity as well as temporal progression of stress symptoms within the population through machine learning methods. 230,400 images representing temporal progression of drought stress symptoms of interspecific turfgrass hybrid mapping population were processed using Python OpenCV and NumPy packages for noise removal, edge-preserving smoothing, color space conversion, contrast enhancement, and identification mapping. Then machine learning-based algorithms and models were developed not only to quantify stress severity but also to monitor temporal progression rate of stress symptoms. Hierarchical clustering was then performed to assess genotypic variation in stress progression. Such machine learning-based high-throughput digital phenotyping platforms can significantly increase the success of quantitative trait locus mapping and candidate gene identification to develop potential molecular markers that will assist in a faster characterization of germplasm to ultimately breed for stress resilient cultivars.

Plant phenotyping relevance

画像処理と機械学習により植物の乾燥ストレス症状の重症度と時間的進行を定量化する高スループット表現型解析プラットフォームが研究の中心であるため、収載。

Code and data availability

The supplied blocks contain only the title page and abstract. They mention 230,400 turfgrass images processed with Python OpenCV/NumPy and machine-learning models, but no data availability statement, repository deposit, or public URL for the images, code, or models is provided. No paper-specific public asset can be ver

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