Application of high-throughput plant phenotyping for assessing biophysical traits and drought response in two oak species under controlled environment

Mazis, Anastasios; Choudhury, Sruti Das; Morgan, Patrick B.; Stoerger, Vincent; Hiller, Jeremy; Ge, Yufeng; Awada, Tala

doi:10.1016/j.foreco.2020.118101

Cited by 37 publications

(18 citation statements)

References 36 publications

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“…For tree improvement and sustainable forest management, the use of image-based high-throughput plant phenotyping was done in two oak species (Quercus bicolor and Quercus prinoides). These species were assessed for drought response and several other biophysical traits (Mazis et al 2020).…”

Section: Phenotyping Of Drought Stress-related Traitsmentioning

confidence: 99%

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

et al. 2021

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Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.

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Section: Phenotyping Of Drought Stress-related Traitsmentioning

confidence: 99%

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

et al. 2021

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“…and Q. prinoides Willd.) under greenhouse conditions [108]. The use of image processing demonstrated changes in the leaf shape and size in some transgenic lines of aspen (Populus tremula L.) carrying the recombinant xyloglucanase gene in the second year of vegetation under semi-natural conditions [109].…”

Section: High-throughput Phenotyping In Gs Of Forest Treesmentioning

confidence: 99%

Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives

Lebedev

Лебедева

Chernodubov

et al. 2020

Forests

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The breeding of forest trees is only a few decades old, and is a much more complicated, longer, and expensive endeavor than the breeding of agricultural crops. One breeding cycle for forest trees can take 20–30 years. Recent advances in genomics and molecular biology have revolutionized traditional plant breeding based on visual phenotype assessment: the development of different types of molecular markers has made genotype selection possible. Marker-assisted breeding can significantly accelerate the breeding process, but this method has not been shown to be effective for selection of complex traits on forest trees. This new method of genomic selection is based on the analysis of all effects of quantitative trait loci (QTLs) using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual. This approach is expected to be much more efficient for forest tree improvement than traditional breeding. Here, we review the current state of the art in the application of genomic selection in forest tree breeding and discuss different methods of genotyping and phenotyping. We also compare the accuracies of genomic prediction models and highlight the importance of a prior cost-benefit analysis before implementing genomic selection. Perspectives for the further development of this approach in forest breeding are also discussed: expanding the range of species and the list of valuable traits, the application of high-throughput phenotyping methods, and the possibility of using epigenetic variance to improve of forest trees.

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“…Due to global climate change, droughts around the world have become more frequent and have increased in severity, which will have a serious impact on the growth of plants and crops (Stocker, 2014;Mazis et al, 2020). In addition, extreme drought and a lack of precipitation are thought to exacerbate climate change (Jia et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy

Liu

Tomasetto

et al. 2022

Front. Plant Sci.

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Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R2 of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring.

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Application of high-throughput plant phenotyping for assessing biophysical traits and drought response in two oak species under controlled environment

Cited by 37 publications

References 36 publications

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives

Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy

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