Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Tao, Haiyu; Xu, Shan; Tian, Yongchao; Li, Zhaofeng; Yan, Guoyong; Zhang, Jiaoping; Wang, Yu; Zhou, Guodong; Deng, Xiong; Zhang, Ze; Ding, Yanfeng; Jiang, Dong; Jin, Shichao

doi:10.1016/j.xplc.2022.100344

Cited by 56 publications

(31 citation statements)

References 442 publications

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“…Areas such as the Mediterranean and southern Europe are experiencing higher impacts of heatdrought stress than other regions, causing major economic and food production losses (EEA, 2020). A 1-3°C rise in mean global air temperature is suggested to decrease wheat production by up to 28% (Shew et al, 2020;Zhang et al, 2022). In fact, in a previous study by our research group on field-grown wheat in Sweden, a yield reduction of up to 40% was observed under combined heat-drought conditions compared with rainy and cold conditions (Lama et al, 2022).…”

Section: Qtlmentioning

confidence: 87%

Developing affordable high-throughput plant phenotyping methods for breeding of cereals and tuber crops

Leiva-Sandoval¹

2023

Acta Universitatis Agriculturae Sueciae

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High-throughput plant phenotyping (HTPP) is a fast, accurate, and non-destructive process for evaluating plants' health and environmental adaptability. HTPP accelerates the identification of agronomic traits of interest, eliminates subjectivism (which is innate to humans), and facilitates the development of adapted genotypes. Current HTPP methods often rely on imaging sensors and computer vision both in the field and under controlled (indoor) conditions. However, their use is limited by the costs and complexity of the necessary instrumentation, data analysis tools, and software. This issue could be overcome by developing more cost-efficient and user-friendly methods that let breeders, farmers, and stakeholders access the benefits of HTPP. To assist such efforts, this thesis presents an ensemble of dedicated affordable phenotyping methods using RGB imaging for a range of key applications under controlled conditions. The affordable Phenocave imaging system for use in controlled conditions was developed to facilitate studies on the effects of abiotic stresses by gathering data on important plant characteristics related to growth, yield, and adaptation to growing conditions and cultivation systems. Phenocave supports imaging sensors including visible (RGB), spectroscopic (multispectral and hyperspectral), and thermal imaging. Additionally, a pipeline for RGB image analysis was implemented as a plugin for the free and easy-to-use software ImageJ. This plugin has since proven to be an accurate alternative to conventional measurements that produces highly reproducible results. A subsequent study was conducted to evaluate the effects of heat and drought stress on plant growth and grain nutrient composition in wheat, an important staple cereal in Sweden. The effects of stress on plant growth were evaluated using image analysis, while stress-induced changes in the abundance of key plant compounds were evaluated by analyzing the nutrient composition of grains via chromatography. This led to the discovery of genotypes whose harvest quality remains stable under heat and drought stress. The next objective was to evaluate biotic stress; for this case, the effect of the fungal disease Fusarium head blight (FHB) that affects grain development in wheat was investigated. For this purpose, seed phenotyping parameters were used to determine the components and settings of a statistical model, which predicts the occurrence of FHB. The results reveal that grain morphology evaluations, such as length and width, were found to be significantly affected by the disease. Another study was carried out to estimate the disease severity of the common scab (CS) in potatoes, a widely popular food source. CS occurs on the tubers and reduces their visual appeal, significantly affecting their market value. Tubers were analyzed by a deep learning-based method to estimate disease lesion areas caused by CS. Results showed a high correlation between the predictions and expert visual scorings of the disease and proved to be a potential tool for the selection of genotypes that fulfill the market standards and resistance to CS. Both case studies highlight the role of imaging in plant health monitoring and its integration into the larger picture of plant health management. The methods presented in this work are a starting point for bridging the gap between costs and accessibility to imaging technology. These are affordable and user-friendly resources for generating pivotal knowledge on plant development and genotype selection. In the future, image acquisition of all the methods can be integrated into the Phenocave system, potentially allowing for a more automated and efficient plant health monitoring process, leading to the identification of tolerant genotypes to biotic and abiotic stresses.

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Section: Qtlmentioning

confidence: 87%

Developing affordable high-throughput plant phenotyping methods for breeding of cereals and tuber crops

Leiva-Sandoval¹

2023

Acta Universitatis Agriculturae Sueciae

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“…For example, imagine the exact analysis performed here applied to the thousands of plant species that occupy both the Great Plains and North American Deserts, or indeed any contrasting set of ecoregions. The decades-long project of herbarium digitization holds the potential for generating massive plant trait datasets with a magnitude of intraspecific scope that has been lacking from previous global trait datasets (Heberling, 2022), and the deployment of high-throughput phenotyping and remote sensing in a range of settings promises to generate equally impressive 'phenomic' datasets (Arend et al, 2022;Cavender-Bares et al, 2022;Gill et al, 2022;Tao et al, 2022). Given the coming tide of truly "big data" plant trait datasets, there is a growing need for creative analytical approaches to synthesizing trait data in ways that will advance our understanding of plant functional trait evolution.…”

Section: Discussionmentioning

confidence: 99%

Applying an interpretable machine learning approach to assess intraspecific trait variation under landscape-scale population differentiation

Majumder

Mason

2023

Preprint

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Premise: Here we demonstrate the application of interpretable machine learning methods to investigate intraspecific functional trait divergence using diverse genotypes of the wide-ranging sunflower Helianthus annuus occupying populations across contrasting ecoregions - the Great Plains versus the North American Deserts. Methods: Recursive feature elimination was applied to functional trait data from the HeliantHome database, followed by the application of Boruta to detect traits most predictive of ecoregion. Random Forest and Gradient Boosting Machine classifiers were then trained and validated, with results visualized using accumulated local effects plots. Key Results: The most ecoregion-predictive functional traits span categories of leaf economics, plant architecture, reproductive phenology, and floral and seed morphology. Relative to the Great Plains, genotypes from the North American Deserts exhibit shorter stature, fewer leaves, higher leaf nitrogen, and longer average length of phyllaries. Conclusions: This approach readily identifies traits predictive of ecoregion origin, and thus functional traits most likely to be responsible for contrasting ecological strategies across the landscape. This type of approach can be used to parse large plant trait datasets in a wide range of contexts, including explicitly testing the applicability of interspecific paradigms at intraspecific scales.

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“…In addition to the exploration of high estimation accuracy, more and more studies are attempting to explore the genetic bases (e.g., heritability) of high-throughput phenotype [ 56 , 62 , 73 ]. CH is a high heritability trait, as effective as yield [ 53 ].…”

Section: Introductionmentioning

confidence: 99%

Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing

et al. 2023

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Canopy height (CH) is an important trait for crop breeding and production. The rapid development of 3D sensing technologies shed new light on high-throughput height measurement. However, a systematic comparison of the accuracy and heritability of different 3D sensing technologies is seriously lacking. Moreover, it is questionable whether the field-measured height is as reliable as believed. This study uncovered these issues by comparing traditional height measurement with four advanced 3D sensing technologies, including terrestrial laser scanning (TLS), backpack laser scanning (BLS), gantry laser scanning (GLS), and digital aerial photogrammetry (DAP). A total of 1920 plots covering 120 varieties were selected for comparison. Cross-comparisons of different data sources were performed to evaluate their performances in CH estimation concerning different CH, leaf area index (LAI), and growth stage (GS) groups. Results showed that 1) All 3D sensing data sources had high correlations with field measurement (r > 0.82), while the correlations between different 3D sensing data sources were even better (r > 0.87). 2) The prediction accuracy between different data sources decreased in subgroups of CH, LAI, and GS. 3) Canopy height showed high heritability from all datasets, and 3D sensing datasets had even higher heritability (H2 = 0.79–0.89) than FM (field measurement) (H2 = 0.77). Finally, outliers of different datasets are analyzed. The results provide novel insights into different methods for canopy height measurement that may ensure the high-quality application of this important trait.

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Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Cited by 56 publications

References 442 publications

Developing affordable high-throughput plant phenotyping methods for breeding of cereals and tuber crops

Developing affordable high-throughput plant phenotyping methods for breeding of cereals and tuber crops

Applying an interpretable machine learning approach to assess intraspecific trait variation under landscape-scale population differentiation

Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing

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