RGB imaging-based evaluation of waterlogging tolerance in cultivated and wild chrysanthemums

Waterlogging is a major stress that impacts the chrysanthemum industry. Large-scale germplasm screening for identifying waterlogging-tolerant resources in a quick and accurate manner is essential for developing new cultivars with improved waterlogging tolerance. To overcome this phenotyping bottleneck, consumer-grade digital cameras have been used to acquire the red-green-blue (RGB) images of 180 chrysanthemum cultivars and their wild relatives under waterlogging stress and well-watered conditions. A total of 103 image-based digital traits (i-traits), including 10 morphological i-traits and 93 texture i-traits, were extracted and systematically analyzed. Most of these i-traits presented high coefficients of variation (<i>CVs</i>) and broad-sense heritability (<i>H</i> <sup><i>2</i></sup> ), with an average <i>CV</i> of 34.04 ​% and an average <i>H</i> <sup><i>2</i></sup> of 0.93. We identified several novel texture i-traits associated with the hue (H) component, which strongly correlated with the traditional waterlogging tolerance index, the membership function value of waterlogging (MFVW) (<i>R</i> ​= ​0.63-0.77). We further employed the random forest (RF) and gradient boosting tree (GBT) machine learning algorithms to predict aboveground biomass and MFVW on the basis of different i-trait datasets. The RF model achieved superior predictive performance, with a coefficient of determination (<i>R</i> <sup><i>2</i></sup> ) of up to 0.88 for shoot weight and 0.86 for MFVW. Moreover, a subset of the top 13 most important i-traits could accurately predict MFVW (<i>R</i> <sup><i>2</i></sup> ​> ​0.80) via the cross-validation method. A total of 10 highly tolerant resources were selected by traditional and RGB-based evaluation, and 50 ​% belonged to <i>Artemisia</i>. Our findings confirmed that RGB-based technology provides a promising novel approach for quantifying waterlogging response that contributes to future breeding programs and genetic dissection for waterlogging tolerance.

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Publication Details

Journal
Plant Phenomics
Published
2025-03-01
DOI
https://doi.org/10.1016/j.plaphe.2025.100019
Citations
1
Primary Topic
Plant responses to water stress
Type
article
Field-Weighted Citation Impact
1.75
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article

RGB imaging-based evaluation of waterlogging tolerance in cultivated and wild chrysanthemums

Limin Zhao, Junwei Zeng, Weimin Fang, Siyue Wang et al.
1 citations
Plant Phenomics
Plant responses to water stress
1.75
article

RGB imaging-based evaluation of waterlogging tolerance in cultivated and wild chrysanthemums

Limin Zhao, Junwei Zeng, Weimin Fang, Siyue Wang, Jiangshuo Su, Fadi Chen, Sumei Chen, Haibin Wang, Fei Zhang, Yang Yang
article en
1 citations

Abstract

Waterlogging is a major stress that impacts the chrysanthemum industry. Large-scale germplasm screening for identifying waterlogging-tolerant resources in a quick and accurate manner is essential for developing new cultivars with improved waterlogging tolerance. To overcome this phenotyping bottleneck, consumer-grade digital cameras have been used to acquire the red-green-blue (RGB) images of 180 chrysanthemum cultivars and their wild relatives under waterlogging stress and well-watered conditions. A total of 103 image-based digital traits (i-traits), including 10 morphological i-traits and 93 texture i-traits, were extracted and systematically analyzed. Most of these i-traits presented high coefficients of variation (<i>CVs</i>) and broad-sense heritability (<i>H</i> <sup><i>2</i></sup> ), with an average <i>CV</i> of 34.04 ​% and an average <i>H</i> <sup><i>2</i></sup> of 0.93. We identified several novel texture i-traits associated with the hue (H) component, which strongly correlated with the traditional waterlogging tolerance index, the membership function value of waterlogging (MFVW) (<i>R</i> ​= ​0.63-0.77). We further employed the random forest (RF) and gradient boosting tree (GBT) machine learning algorithms to predict aboveground biomass and MFVW on the basis of different i-trait datasets. The RF model achieved superior predictive performance, with a coefficient of determination (<i>R</i> <sup><i>2</i></sup> ) of up to 0.88 for shoot weight and 0.86 for MFVW. Moreover, a subset of the top 13 most important i-traits could accurately predict MFVW (<i>R</i> <sup><i>2</i></sup> ​> ​0.80) via the cross-validation method. A total of 10 highly tolerant resources were selected by traditional and RGB-based evaluation, and 50 ​% belonged to <i>Artemisia</i>. Our findings confirmed that RGB-based technology provides a promising novel approach for quantifying waterlogging response that contributes to future breeding programs and genetic dissection for waterlogging tolerance.

Plant PhenomicsVol. 7(1)
Nanjing Agricultural University (CN), Zhongshan Hospital (CN), State Forestry and Grassland Administration (CN), Ministry of Agriculture and Rural Affairs (CN)
Zero hunger
Openalex Percentile: Top 17%
Plant responses to water stress
1.75
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