Comprehensive Morphometric Analysis of Apple Fruits and Weighted Class Assignation using Machine Learning

Dujak, Christian; Jurado, Francisco; Aranzana, María José

doi:10.21203/rs.3.rs-2860631/v1

Cited by 5 publications

(7 citation statements)

References 25 publications

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“…In this study, we used the apple images generated by Dujak et al. [ 23 ]. Briefly, the images contained from 6 to 10 apple halves from 356 genotypes of the apple reference collection (Apple RefPop) [ 24 ] grown at the Institut de Recerca i Tecnologia Agroalimentàries experimental field of Gimenells (Lleida, Spain).…”

Section: Methodsmentioning

confidence: 99%

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GenoDrawing: An Autoencoder Framework for Image Prediction from SNP Markers

Jurado-Ruiz,

Rousseau,

Botía

et al. 2023

Plant Phenomics

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Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks (NNs), particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, an NN model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrates proficiency in its task using a small dataset of shape-related SNPs. Results indicate that the use of SNPs associated with visual traits has substantial impact on the generated images, consistent with biological interpretation. While using substantial SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically substantial heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and dataset balance in terms of phenotypes for more precise outcomes.

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Section: Methodsmentioning

confidence: 99%

“…The images were produced in Dujak et al. [ 23 ], from where the images can be obtained under request.…”

Section: Data Availabilitymentioning

confidence: 99%

GenoDrawing: An Autoencoder Framework for Image Prediction from SNP Markers

Jurado-Ruiz,

Rousseau,

Botía

et al. 2023

Plant Phenomics

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“…Phenotypic data were extracted from 23 , and consisted on four size and 10 morphometric descriptors obtained using the Tomato Analyzer software Version 3 developed by 26 (Supplementary information 2). The data were collected in 12,692 apple sections harvested over three seasons: 2018 (134 genotypes), 2019 (274 genotypes) and 2020 (339 genotypes).…”

Section: Phenotypic Datamentioning

confidence: 99%

“…The traits evaluated are broadly described in Dujak et al 23 . For each trait, density plots were created to visualize how the data was distributed in each specific year.…”

Section: Phenotypic Datamentioning

confidence: 99%

“…To enhance our understanding of the genomic regions, markers, and genes responsible for the inherited natural variation of fruit morphology, we conducted a GWAS study using fruit measurements obtained for a comprehensive description of fruit shape and size by 23 in the densely genotyped apple REFPOP collection 7,8 . Also, whole genome RNAseq data served to propose candidate genes that will require further validation.…”

Section: Introductionmentioning

confidence: 99%

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Genomic analysis of fruit size and shape traits in apple: unveiling candidate genes through GWAS analysis

Dujak,

Aranzana

2023

Preprint

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Genomic tools facilitate the efficient selection of improved genetic materials with within a breeding program. In this work, we focused on two apple fruit quality traits: shape and size. We utilized data from 11 fruit morphology parameters gathered across three years of harvest from 355 genotypes of the Apple REFPOP collection, which serves as a representative sample of the genetic variability present in European cultivated apples. The data was then employed for genome-wide association analysis (GWAS) using the FarmCPU and the BLINK models. The analysis identified 59 SNPs associated with fruit size and shape traits (35 with FarmCPU and 45 with BLINK) responsible for 71 QTNs. These QTNs were distributed across all chromosomes except for chromosome10 and 15. Thirty-four QTNs, identified by 27 SNPs, were related for size traits and thirty-seven QTNs, identified by 26 SNPs, were related to shape attributes. The definition of the haploblocks containing the most relevant SNPs served to propose candidate genes, among them the genes of the ovate family protein MdOFP17 and MdOFP4 which were in a 9.7kb haploblock on chromosome 11. RNA-seq data revealed low or null expression of these genes in the oblong cultivar ‘Skovfoged” and higher expression in the flat ‘Grand’mere’. In conclusion, this comprehensive GWAS analysis of the Apple REFPOP collection has revealed promising genetic markers and candidate genes associated with apple fruit shape and size attributes, providing valuable insights that could enhance the efficiency of future breeding programs.

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FruitPhenoBox – a device for rapid and automated fruit phenotyping of small sample sizes

Kirchgessner,

Hodel,

Studer

et al. 2024

Plant Methods

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Background Fruit appearance of apple (Malus domestica Borkh.) is accession-specific and one of the main criteria for consumer choice. Consequently, fruit appearance is an important selection criterion in the breeding of new cultivars. It is also used for the description of older varieties or landraces. In commercial apple production, sorting devices are used to classify large numbers of fruit from a few cultivars. In contrast, the description of fruit from germplasm collections or breeding programs is based on only a few fruit from many accessions and is mostly performed visually by pomology experts. Such visual ratings are laborious, often difficult to compare and remain subjective. Results Here we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit. Recording of approximately 100 fruit from each of 15 apple cultivars using the FruitPhenoBox was rapid, with an average handling and recording time of less than eleven seconds per fruit. Comparison of fruit images from the 15 apple cultivars identified significant differences in shape index, fruit width, height and weight. Fruit shape was characteristic for each cultivar, while fruit color showed larger variation within sample sets. Assessing a subset of 20 randomly selected fruit per cultivar, fruit height, width and weight were described with a relative margin of error of 2.6%, 2.2%, and 6.2%, respectively, calculated from the mean value of all available fruit. Conclusions The FruitPhenoBox allows for the rapid and consistent description of fruit appearance from individual apple accessions. By relating the relative margin of error for fruit width, height and weight description with different sample sizes, it was possible to determine an appropriate fruit sample size to efficiently and accurately describe the recorded traits. Therefore, the FruitPhenoBox is a useful tool for breeding and the description of apple germplasm collections.

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Comprehensive Morphometric Analysis of Apple Fruits and Weighted Class Assignation using Machine Learning

Cited by 5 publications

References 25 publications

GenoDrawing: An Autoencoder Framework for Image Prediction from SNP Markers

GenoDrawing: An Autoencoder Framework for Image Prediction from SNP Markers

Genomic analysis of fruit size and shape traits in apple: unveiling candidate genes through GWAS analysis

FruitPhenoBox – a device for rapid and automated fruit phenotyping of small sample sizes

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