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Your search keyword '"Liu, Liantao"' showing total 14 results
Start Over Author "Liu, Liantao" Journal frontiers in plant science
14 results on '"Liu, Liantao"'

5. RhizoPot platform: A high-throughput in situ root phenotyping platform with integrated hardware and software

Author

Zhao, Hongjuan, Wang, Nan, Sun, Hongchun, Zhu, Lingxiao, Zhang, Ke, Zhang, Yongjiang, Zhu, Jijie, Li, Anchang, Bai, Zhiying, Liu, Xiaoqing, Dong, Hezhong, Liu, Liantao, and Li, Cundong

Subjects
Plant Science
Abstract

Quantitative analysis of root development is becoming a preferred option in assessing the function of hidden underground roots, especially in studying resistance to abiotic stresses. It can be enhanced by acquiring non-destructive phenotypic information on roots, such as rhizotrons. However, it is challenging to develop high-throughput phenotyping equipment for acquiring and analyzing in situ root images of root development. In this study, the RhizoPot platform, a high-throughput in situ root phenotyping platform integrating plant culture, automatic in situ root image acquisition, and image segmentation, was proposed for quantitative analysis of root development. Plants (1-5) were grown in each RhizoPot, and the growth time depended on the type of plant and the experimental requirements. For example, the growth time of cotton was about 110 days. The imaging control software (RhizoAuto) could automatically and non-destructively image the roots of RhizoPot-cultured plants based on the set time and resolution (50-4800 dpi) and obtain high-resolution (>1200 dpi) images in batches. The improved DeepLabv3+ tool was used for batch processing of root images. The roots were automatically segmented and extracted from the background for analysis of information on radical features using conventional root software (WinRhizo and RhizoVision Explorer). Root morphology, root growth rate, and lifespan analysis were conducted using in situ root images and segmented images. The platform illustrated the dynamic response characteristics of root phenotypes in cotton. In conclusion, the RhizoPot platform has the characteristics of low cost, high-efficiency, and high-throughput, and thus it can effectively monitor the development of plant roots and realize the quantitative analysis of root phenotypes in situ.

Published
2022

7. Seed Priming With Melatonin Promotes Seed Germination and Seedling Growth of Triticale hexaploide L. Under PEG-6000 Induced Drought Stress

Author

Guo, Yuhui, primary, Li, Dongxiao, additional, Liu, Liantao, additional, Sun, Hongchun, additional, Zhu, Lingxiao, additional, Zhang, Ke, additional, Zhao, Haiming, additional, Zhang, Yongjiang, additional, Li, Anchang, additional, Bai, Zhiying, additional, Tian, Liwen, additional, Dong, Hezhong, additional, and Li, Cundong, additional

Published
2022
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8. Wheat Escapes Low Light Stress by Altering Pollination Types

Author

Yang, Hong, primary, Li, Yongpeng, additional, Li, Dongxiao, additional, Liu, Liantao, additional, Qiao, Yunzhou, additional, Sun, Hongyong, additional, Liu, Wenwen, additional, Qiao, Wenjun, additional, Ma, Yuzhao, additional, Liu, Mengyu, additional, Li, Cundong, additional, and Dong, Baodi, additional

Published
2022
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14. Predicting F v /F m and evaluating cotton drought tolerance using hyperspectral and 1D-CNN.

Author

Guo C, Liu L, Sun H, Wang N, Zhang K, Zhang Y, Zhu J, Li A, Bai Z, Liu X, Dong H, and Li C

Abstract

The chlorophyll fluorescence parameter F v /F m is significant in abiotic plant stress. Current acquisition methods must deal with the dark adaptation of plants, which cannot achieve rapid, real-time, and high-throughput measurements. However, increased inputs on different genotypes based on hyperspectral model recognition verified its capabilities of handling large and variable samples. F v /F m is a drought tolerance index reflecting the best drought tolerant cotton genotype. Therefore, F v /F m hyperspectral prediction of different cotton varieties, and drought tolerance evaluation, are worth exploring. In this study, 80 cotton varieties were studied. The hyperspectral cotton data were obtained during the flowering, boll setting, and boll opening stages under normal and drought stress conditions. Next, One-dimensional convolutional neural networks (1D-CNN), Categorical Boosting (CatBoost), Light Gradient Boosting Machines (LightBGM), eXtreme Gradient Boosting (XGBoost), Decision Trees (DT), Random Forests (RF), Gradient elevation decision trees (GBDT), Adaptive Boosting (AdaBoost), Extra Trees (ET), and K-Nearest Neighbors (KNN) were modeled with F v /F m . The Savitzky-Golay + 1D-CNN model had the best robustness and accuracy (RMSE = 0.016, MAE = 0.009, MAPE = 0.011). In addition, the F v /F m prediction drought tolerance coefficient and the manually measured drought tolerance coefficient were similar. Therefore, cotton varieties with different drought tolerance degrees can be monitored using hyperspectral full band technology to establish a 1D-CNN model. This technique is non-destructive, fast and accurate in assessing the drought status of cotton, which promotes smart-scale agriculture., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Guo, Liu, Sun, Wang, Zhang, Zhang, Zhu, Li, Bai, Liu, Dong and Li.)

Published
2022
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