Advances in optical phenotyping of cereal crops

Sun, Dawei; Robbins, Kelly R.; Morales, Nicolás; Shu, Qingyao; Cen, Haiyan

doi:10.1016/j.tplants.2021.07.015

Cited by 78 publications

(46 citation statements)

References 161 publications

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“…Glyphosate affects the photosynthetic activity of plants by inhibiting the shikimic acid pathway ( Gomes et al, 2014 ), which is eventually reflected in leaf surface reflectance. At present, hyperspectral technology has been widely used in the early detection of stress due to its high throughput, rapid and non-destructive nature ( Sun et al, 2021 ; Sarić et al, 2022 ). Visible near infrared spectroscopy can capture the changes in leaf reflectance in time so as to realize the identification of resistance cultivars.…”

Section: Discussionmentioning

confidence: 99%

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Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Tao

Bai

et al. 2022

Front. Plant Sci.

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Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other’s advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.

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Section: Discussionmentioning

confidence: 99%

“…Leaf surface reflectance provides a wide perspective for plant growth conditions ( Sun et al, 2021 ). As a derivative index of leaf surface reflectance, the spectral index has been widely used in crop phenotypic monitoring such as stress perception and variety identification ( Feng et al, 2019 ; Shirzadifar et al, 2020b ).…”

Section: Methodsmentioning

confidence: 99%

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Tao

Bai

et al. 2022

Front. Plant Sci.

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“…Phenotypic indicators with strongly inherited traits are being investigated. Furthermore, phenotypic and genetic variation can be interpreted using optical sensing-based phenotyping (OSP) data analysis (Xavier et al, 2017 ; Sun et al, 2021 ).…”

Section: Research Status Of High-throughput Phenotypic Information Of...mentioning

confidence: 99%

The field phenotyping platform's next darling: Dicotyledons

Chen

et al. 2022

Front. Plant Sci.

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The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

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“…Nowadays, there are some high-throughput phenotyping technologies that have been introduced into plant improvement which allow data acquisition in a rapid and noninvasive way. These high-throughput phenotyping methods always rely on dynamic optical imaging equipment and machine learning [150]. Take its application in pathogen resistance, for instance: hyperspectral imaging was used to assess disease severity, such as cellular level changes in barley leaves infected with powdery mildew [151,152]; an automated imaging scanner was used to assess resistance against Septoria tritici blotch (STB, caused by fungus Zymoseptoria tritici) in wheat in a field experiment, with 26 chromosomal intervals harbouring several novel loci identified, showing quantitative resistance emerging from co-evolution with the pathogen [153].…”

Section: High-throughput Phenotypingmentioning

confidence: 99%

Understanding R Gene Evolution in Brassica

et al. 2022

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Brassica crop diseases caused by various pathogens, including viruses, bacteria, fungi and oomycetes, have devastating effects on the plants, leading to significant yield loss. This effect is worsened by the impact of climate change and the pressure to increase cultivation worldwide to feed the burgeoning population. As such, managing Brassica diseases has become a challenge demanding a rapid solution. In this review, we provide a detailed introduction of the plant immune system, discuss the evolutionary pattern of both dominant and recessive disease resistance (R) genes in Brassica and discuss the role of epigenetics in R gene evolution. Reviewing the current findings of how R genes evolve in Brassica spp. provides further insight for the development of creative ideas for crop improvement in relation to breeding sustainable, high quality, disease-resistant Brassica crops.

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Advances in optical phenotyping of cereal crops

Cited by 78 publications

References 161 publications

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

The field phenotyping platform's next darling: Dicotyledons

Understanding R Gene Evolution in Brassica

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