Image features and DUS testing traits for peanut pod variety identification and pedigree analysis

Deng, Limiao; Han, Zhongzhi

doi:10.1002/jsfa.9472

Cited by 16 publications

(20 citation statements)

References 13 publications

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“…Given that DUS testing is based on the apparent morphological characteristics of the study plants, the results and comparative analysis of candidate, standard, and approximate varieties will be influenced by environmental factors [8]. In addition, different testers may subjectively perceive traits differently, leading to inconsistencies in the evaluation of certain traits [9]. Moreover, the substantial workload involved further increases the likelihood of human error in DUS testing.…”

Section: Introductionmentioning

confidence: 99%

Genetic diversity and fingerprinting of 33 standard flue-cured tobacco varieties for use in distinctness, uniformity, and stability testing

Geng

Cheng

et al. 2020

BMC Plant Biol

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Background: At present, the distinctness, uniformity, and stability (DUS) testing of flue-cured tobacco (Nicotiana tabacum L.) depends on field morphological identification, which is problematic in that it is labor intensive, timeconsuming, and susceptible to environmental impacts. In order to improve the efficiency and accuracy of tobacco DUS testing, the development of a molecular marker-based method for genetic diversity identification is urgently needed. Results: In total, 91 simple sequence repeats (SSR) markers with clear and polymorphic amplification bands were obtained with polymorphism information content, Nei index, and Shannon information index values of 0.3603, 0.4040, and 0.7228, respectively. Clustering analysis showed that the 33 study varieties, which are standard varieties for flue-cured tobacco DUS testing, could all be distinguished from one another. Further analysis showed that a minimum of 25 markers were required to identify the genetic diversity of these varieties. Following the principle of two markers per linkage group, 48 pairs of SSR markers were selected. Correlation analysis showed that the genetic relationships revealed by the 48 SSR markers were consistent with those found using the 91 SSR markers. Conclusions: The genetic fingerprints of the 33 standard varieties of flue-cured tobacco were constructed using 48 SSR markers, and an SSR marker-based identification technique for new tobacco varieties was developed. This study provides a reliable technological approach for determining the novelty of new tobacco varieties and offers a solid technical basis for the accreditation and protection of new tobacco varieties.

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Section: Introductionmentioning

confidence: 99%

Genetic diversity and fingerprinting of 33 standard flue-cured tobacco varieties for use in distinctness, uniformity, and stability testing

Geng

Cheng

et al. 2020

BMC Plant Biol

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“…K‐means clustering showed presence of distinct phenotypic groups of genotypes in this panel. The robustness of this approach in genotype clustering was previously demonstrated in maize (Ma, Cheng, & Zhang, 2014), soybean (Leite, Unêda‐Trevisoli, da Silva, da Silva, & Di Mauro, 2018) and peanut (Deng & Han, 2019). The decision on selection of which covariates should be added to DUS‐traits enhanced genomic prediction was supported by the results of stepwise selection in which all combinations of traits were used to predict the response traits grain yield and plant height (Burnham & Anderson, 2004).…”

Section: Discussionmentioning

confidence: 86%

Optimising accuracy of performance predictions using available morphophysiological information in wheat breeding germplasm

Guberac

Galić

Rebekić

et al. 2021

Annals of Applied Biology

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Wheat breeding programmes often harbour large number of developed progenies. Testing of all progenies in many target environments is not considered cost‐effective, so the genomic predictions are employed. Genomic predictions might be enhanced with adaptational traits given the cost‐effectiveness of their phenotyping. The aim of this study was to assess effectiveness of adding a priori available phenotyping data of adaptational morpho‐physiological traits to genomic predictions in Bayesian framework. The panel of 120 winter wheat genotypes was sown over four growing seasons (2013/2014–2016/2017) in field conditions in two replicates and phenotyped for target traits grain yield and plant height along with nine distinctness, uniformity and stability (DUS) traits. Five genotype groups were defined by the K‐means clustering method based on nine DUS traits. DUS traits were used as fixed covariates in five different genomic prediction models with different assumptions about the distribution densities of marker effects: Bayes A, Bayes B, Bayes C, Bayesian ridge regression and Bayesian lasso. Adding DUS traits as covariates significantly improved accuracies and reduced the root mean square errors (RMSEP) of genomic predictions. Marginal differences between different models were observed. Adding covariates to genomic prediction models might be good strategy to improve accuracies of the predictions by accounting for environmental adaptations, provided their a priori availability or low costs of additional phenotyping.

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“…In the early years, manual observation with a microscope was used to detect rice blast . Later came the hyperspectral detection method and the morphological image detection method . Nevertheless, these methods detect lesions on the surface of rice, and these are easy to confuse with other diseases, and so the accuracy may not be high enough.…”

Section: Introductionmentioning

confidence: 99%

A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

Yang

Wang

et al. 2020

J Sci Food Agric

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BACKGROUND Rice blast fungus is a worldwide disease, and it is one of the most serious rice diseases in the north and south rice fields in China. The initial symptoms of rice blast are not obvious, and the speed of transmission is fast. Manual identification is time‐consuming and laborious. At present, it is a great challenge to realize rapid and accurate early identification of rice blast. RESULTS In this paper, an identification method based on crop disease spores' diffraction fingerprint texture for rice blast was studied; this method utilizes the light field and texture features of diffraction images. To verify the reliability of the model that we proposed, we selected two methods of manual identification and machine recognition to compare and detect rice blast spores. The experimental results show that the identification of light diffraction characteristics is not only higher than the traditional manual recognition by microscope (increased by more than 0.3%), but also faster after neural network training (increased by more than 90%). The diffraction recognition method used in this study, based on crop disease spores' diffraction fingerprint texture, can be completed in a few seconds, and its test accuracy is 97.18%. CONCLUSION The proposed method, a rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture, has certain advantages compared with the existing manual identification by microscope. This method can be applied to the recognition of rice blast in agricultural research. © 2020 Society of Chemical Industry

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Image features and DUS testing traits for peanut pod variety identification and pedigree analysis

Cited by 16 publications

References 13 publications

Genetic diversity and fingerprinting of 33 standard flue-cured tobacco varieties for use in distinctness, uniformity, and stability testing

Genetic diversity and fingerprinting of 33 standard flue-cured tobacco varieties for use in distinctness, uniformity, and stability testing

Optimising accuracy of performance predictions using available morphophysiological information in wheat breeding germplasm

A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

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