Research Article | Open Access

Volume 2025 |Article ID 100019 | https://doi.org/10.1016/j.plaphe.2025.100019

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

Siyue Wang,¹ Yang Yang,¹ Junwei Zeng,¹ Limin Zhao,¹ Haibin Wang,¹ Sumei Chen,^1,2 Weimin Fang,^1,2 Fei Zhang,^1,2 Jiangshuo Su ,¹ Fadi Chen ^1,2

¹State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Landscaping, Key Laboratory of Flower Biology and Germplasm Innovation, Ministry of Agriculture and Rural Affairs, Key Laboratory of State Forestry and Grassland Administration on Biology of Ornamental Plants in East China, College of Horticulture, Nanjing Agricultural University, No.1 Weigang, Nanjing, 210095, China
²Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing, 210014, China

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 (CVs) and broad-sense heritability (H2), with an average CV of 34.04 % and an average H2 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) (R = 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 (R2) 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 (R2 > 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 Artemisia. 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.