Changes in Vertical Phenotypic Traits of Rice (Oryza sativa L.) Response to Water Stress

Zhang, Yufan; Jin, Xiuliang; Wang, Yu; Qiao, Han

doi:10.3389/fpls.2022.942110

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…Currently, plant phenomics primarily supports breeding research in major crops such as rice, wheat, and maize [ 6 ]. The focus lies in identifying desirable crop traits by analyzing phenotypic variations, throughout the growth and development stages, considering biotic and abiotic stress factors, as well as various nutritional conditions [ 4 , 7 – 9 ]. Combined with the high-throughput genomics technology and algorithms, plant phenomics can further assist in bridging the gaps between genotype and phenotype, which would help to hasten crop breeding.…”

Section: Introductionmentioning

confidence: 99%

Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

Chen,

Wang,

et al. 2023

Plant Phenomics

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Plant phenomics aims to perform high-throughput, rapid, and accurate measurement of plant traits, facilitating the identification of desirable traits and optimal genotypes for crop breeding. Salvia miltiorrhiza (Danshen) roots possess remarkable therapeutic effect on cardiovascular diseases, with huge market demands. Although great advances have been made in metabolic studies of the bioactive metabolites, investigation for S . miltiorrhiza roots on other physiological aspects is poor. Here, we developed a framework that utilizes image feature extraction software for in-depth phenotyping of S . miltiorrhiza roots. By employing multiple software programs, S. miltiorrhiza roots were described from 3 aspects: agronomic traits, anatomy traits, and root system architecture. Through K -means clustering based on the diameter ranges of each root branch, all roots were categorized into 3 groups, with primary root-associated key traits. As a proof of concept, we examined the phenotypic components in a series of randomly collected S . miltiorrhiza roots, demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation ( R 2 = 0.8312), which was sufficient for subsequently estimating the production of bioactive metabolites without content determination. This study provides an important approach for further grading of medicinal materials and breeding practices.

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Section: Introductionmentioning

confidence: 99%

Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

Chen,

Wang,

et al. 2023

Plant Phenomics

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Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

Okyere,

Cudjoe,

Virlet

et al. 2024

Remote Sensing

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Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

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Changes in Vertical Phenotypic Traits of Rice (Oryza sativa L.) Response to Water Stress

Cited by 2 publications

References 55 publications

Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

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