High throughput phenotyping dataset related to seed and seedling traits of sugar beet genotypes

Ducournau, Sylvie; Charrier, Aurélie; Demilly, Didier; Wagner, Marie-Hélène; Trigui, G.; Dupont, Audrey; Hamdy, Sherif; Boudehri-Giresse, Karima; Corre, Laurence Le; Landais, Laurence; Delanoue, Angélique; Charruaud, Dorothée; Henry, Karine; Henry, Nicolás; Ledroit, Lydie; Dürr, Carolyne

doi:10.1016/j.dib.2020.105201

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…Some datasets are useful to compare variance in seed morphological traits ( Ducournau et al, 2020 ), while others can be used for the development of computer vision tools for fruit counting and automatic quality assessment. In this category, there is a soybean image dataset to assess seed damage from mechanical and biological sources ( Pereira et al, 2019 ), a dataset for the identification of Indian basmati rice ( Oryza sativa ) seed varieties ( Sharma et al, 2020 ), sugar beet ( Beta vulgaris ) seed traits ( Ducournau et al, 2020 ), a cocoa bean ( Theobroma cacao ) dataset for quality assessment ( Santos et al, 2019 ), a banana ( Musa sp. ) tier abnormality classification ( Piedad, 2019 ), and hyperspectral images of different loose tea ( Camellia sinensis ; Mishra, 2018 ; Supplemental Data Set 5 ).…”

Section: Applications Of Htpmentioning

confidence: 99%

“…Even though the majority of available datasets are lacking a clear description of conditions depicted, it is important that new datasets include metadata and methods to collect environmental data in their experimental design. Mathematical models, machine learning, and most recently deep learning models, can be used as guides to identify stress and predict crop performance under defined conditions ( Bai et al, 2016 ; Atkinson et al, 2017a ; Joalland et al, 2017 ; Moghadam et al, 2017 ; Naito et al, 2017 ; Fernandez-Gallego et al, 2018 ; Prey et al, 2019 ; Walter et al, 2019 ; Ducournau et al, 2020 ; Kerkech et al, 2020 ; Selvaraj et al, 2020 ). Deep learning models have the advantage of automatically extracting features from the image by constructing increasingly abstract representations of the relationships within the dataset ( LeCun et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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Resources for image-based high-throughput phenotyping in crops and data sharing challenges

et al. 2021

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High throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors such as RGB cameras, hyperspectral sensors and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterise crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalising between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement. We emphasise the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

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Section: Applications Of Htpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

et al. 2021

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“…Due to the lack of standardised X-ray imaging protocols, the choices of certain imaging parameters were adapted by the experimenters depending on the seed species (density, size and number of seeds) as well as the trait to observe [ 6 ]. Although the use of tomography for seed analysis has been developing in recent years [ 12 , 23 – 26 ], 2D radiography remains a simpler, cost-effective, faster and therefore the technology which minimizes the dose by comparison with tomography. It has been widely used for many years to assess seed quality [ 27 , 28 ] and identify mechanical or insect damage [ 11 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning

Hamdy,

Charrier,

Corre

et al. 2024

Plant Methods

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Background The detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article. Results 2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating. Conclusion This work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.

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“…Developments in genotyping technologies are complemented by phenotyping methods targeted at producing comprehensive and precise characterization data of germplasm collections. These methods include automated phenotyping, that may be employed to measure with high precision complex traits of agronomic relevance including root traits [31], fruit traits [32], and seed traits [33]. Any of these traits can then be combined with SNP data to identify MTAs that underpin genomic loci of interest [34] and thus project their relevance into breeding decisions.…”

Section: Introductionmentioning

confidence: 99%

Genome wide association study of agronomic and seed traits in a world collection of proso millet (Panicum miliaceum L.)

et al. 2021

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Background The climate crisis threatens sustainability of crop production worldwide. Crop diversification may enhance food security while reducing the negative impacts of climate change. Proso millet (Panicum milaceum L.) is a minor cereal crop which holds potential for diversification and adaptation to different environmental conditions. In this study, we assembled a world collection of proso millet consisting of 88 varieties and landraces to investigate its genomic and phenotypic diversity for seed traits, and to identify marker-trait associations (MTA). Results Sequencing of restriction-site associated DNA fragments yielded 494 million reads and 2,412 high quality single nucleotide polymorphisms (SNPs). SNPs were used to study the diversity in the collection and perform a genome wide association study (GWAS). A genotypic diversity analysis separated accessions originating in Western Europe, Eastern Asia and Americas from accessions sampled in Southern Asia, Western Asia, and Africa. A Bayesian structure analysis reported four cryptic genetic groups, showing that landraces accessions had a significant level of admixture and that most of the improved proso millet materials clustered separately from landraces. The collection was highly diverse for seed traits, with color varying from white to dark brown and width spanning from 1.8 to 2.6 mm. A GWAS study for seed morphology traits identified 10 MTAs. In addition, we identified three MTAs for agronomic traits that were previously measured on the collection. Conclusion Using genomics and automated seed phenotyping, we elucidated phylogenetic relationships and seed diversity in a global millet collection. Overall, we identified 13 MTAs for key agronomic and seed traits indicating the presence of alleles with potential for application in proso breeding programs.

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High throughput phenotyping dataset related to seed and seedling traits of sugar beet genotypes

Cited by 6 publications

References 8 publications

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning

Genome wide association study of agronomic and seed traits in a world collection of proso millet (Panicum miliaceum L.)

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