Pubescence color classification in soybean breeding using aerial images and the Random Forest machine learning algorithm.

Abstract

max L. Merr.) pubescence color is a trait commonly recorded by breeding programs. In previous research using high-throughput phenotyping (HTP), researchers could separate gray pubescence from light tawny and tawny pubescence, but could not separate light tawny from tawny. Using the Random Forest algorithm and time series of aerial RGB (red, green, blue) and multispectral images, this study aimed to classify pubescence color by testing models in data subsets from experiments grown over three years. By incorporating the pubescence color of the parental lines and training the model with a time series of images (four drone flights before or at maturity), a higher overall accuracy was achieved compared to a single flight at maturity. The red/blue index was the most successful feature for discriminating pubescence color, and the blue normalized difference vegetation index (NDVI) and green NDVI were also helpful, mainly in discriminating gray from light tawny pubescence. The overall accuracy was 86.55% in the best scenario (Kappa = 0.7976), and the sensitivity for gray, light tawny, and tawny pubescence were 0.893, 0.788, and 0.915, respectively. When models were tested in an independent environment, they achieved a lower overall accuracy of 65.86%, but still demonstrated fair to good model reliability (Kappa = 0.4874). Applying an HTP pipeline, as used in this study, would help breeding programs save time classifying pubescence color. Since pod color interferes with this trait in the background, genotyping a proportion of the plant rows for both traits and phenotyping pod color could improve the results.

Plant phenotyping relevance

航空画像とRandom Forestを用いてダイズの毛茸色という植物形質を分類し、時系列画像、特徴量、独立環境での精度を検証したHTP手法研究であり、表現型取得・抽出法が中心である。

Code and data availability

The article describes UAV imagery, field pubescence notes, and an RF pipeline, but provides no public deposit of the phenotype data, images, or author analysis code. The only supplementary mention is a generic 'Supplementary data' link to the article DOI, with no stated contents of datasets or code. All repository URLs

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