Comprehensive phenotyping reveals interactions and functions of <i>Arabidopsis thaliana</i> TCP genes in yield determination

Es, Sam W. van; Auweraert, Elwin B. van der; Silveira, Sylvia Rodrigues da; Angenent, Gerco C.; Dijk, Aalt D. J. van; Immink, Richard G. H.

doi:10.1111/tpj.14326

Cited by 22 publications

(10 citation statements)

References 51 publications

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“…The dynamic growth patterns were observed in Arabidopsis day and night ( Wiese et al, 2007 ; van Es et al, 2019 ) and demonstrated that daylight growth was responsible for 70% of growth activities ( Wiese et al, 2007 ). The overall growth pattern of the selected plant showed a somewhat linear trend for multiple growth stages ( Figure 2B ) and agreed well with previous studies on Arabidopsis ( van Es et al, 2019 ). Three of 122 plant samples were selected and the dynamic growth pattern of the individual plants was compared ( Figure 2C ).…”

Section: Resultsmentioning

confidence: 99%

“…Arabidopsis thaliana was selected because it is a model plant for scientists and is rich in several noteworthy information. Moreover, the growth pattern of the gene function ( van Es et al, 2019 ) and stress responses ( Dhondt et al, 2014 ) was analyzed using time series.…”

Section: Discussionmentioning

confidence: 99%

“…Plant phenotype includes multiple aspects of plant science and its definitions vary in different plant science-related fields ( Tardieu et al, 2017 ). Automated high-throughput phenotyping platform (HTPP) generates high-quality data ( Lee et al, 2018 ) from multiple sensors ( Fahlgren et al, 2015 ) and yields the complete life cycle of a plant ( van Es et al, 2019 ). Moreover, rich phenotype data, based on time series generated from a single plant captured by HTPP, can provide insights into traits of interest.…”

Section: Introductionmentioning

confidence: 99%

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Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

et al. 2021

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Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15–21 DAS) and late (∼21–23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17–21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

et al. 2021

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“…The activity of Arabidopsis thaliana gene promoters was predicted using ML, based on the presence of cis-regulatory elements (Uygun et al, 2019). Related to this, (Washburn et al, 2019) applied DL to predict maize gene expression levels.…”

Section: Ll Open Accessmentioning

confidence: 99%

“…As illustrated in Figure 3 , the different macroscopic levels can be studied using different imaging systems. Development at the level of plant organs can be studied in detail using microscope setups ( Dhondt et al., 2013 ), growth of the full plant can be phenotyped in growth chambers ( van Es et al, 2019 ) or using robotic devices in greenhouses and open fields, e.g ( van der Heijden et al, 2012 ; Virlet et al, 2017 ), and drones can be used to inspect plots or complete fields, e.g ( Araus et al., 2018 ). Different imaging sensors exist, measuring different parts of the electromagnetic spectrum, such as red-green-blue color, multispectral and hyperspectral, long-wave infrared (thermal), chlorophyll-fluorescence, and tomography (magenetic resonance imaging [MRI], positron emission tomography [PET], computerized tomography [CT]) ( Li et al., 2014 ).…”

Section: Machine Learning For Macroscopic Phenotypesmentioning

confidence: 99%

Machine learning in plant science and plant breeding

et al. 2021

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Summary Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

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Genetic dissection of morphological variation in rosette leaves and leafy heads in cabbage (Brassica oleracea var. capitata)

et al. 2022

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Key message Correlations between morphological traits of cabbage rosette leaves and heads were found. Genome-wide association studies of these traits identified 50 robust quantitative trait loci in multiple years. Half of these loci affect both organs. Abstract Cabbage (Brassica oleracea var. capitata) is an economically important vegetable crop cultivated worldwide. Cabbage plants go through four vegetative stages: seedling, rosette, folding and heading. Rosette leaves are the largest leaves of cabbage plants and provide most of the energy needed to produce the leafy head. To understand the relationship and the genetic basis of leaf development and leafy head formation, 308 cabbage accessions were scored for rosette leaf and head traits in three-year field trials. Significant correlations were found between morphological traits of rosette leaves and heads, namely leaf area with the head area, height and width, and leaf width with the head area and head height, when heads were harvested at a fixed number of days after sowing. Fifty robust quantitative trait loci (QTLs) for rosette leaf and head traits distributed over all nine chromosomes were identified with genome-wide association studies. All these 50 loci were identified in multiple years and generally affect multiple traits. Twenty-five of the QTL were associated with both rosette leaf and leafy head traits. We discuss thirteen candidate genes identified in these QTL that are expressed in heading leaves, with an annotation related to auxin and other phytohormones, leaf development, and leaf polarity that likely play a role in leafy head development or rosette leaf expansion.

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Comprehensive phenotyping reveals interactions and functions of Arabidopsis thaliana TCP genes in yield determination

Cited by 22 publications

References 51 publications

Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

Machine learning in plant science and plant breeding

Genetic dissection of morphological variation in rosette leaves and leafy heads in cabbage (Brassica oleracea var. capitata)

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