In the article titled“Automatic Fruit Morphology Phenome and Genetic Analysis:An Application in the Octoploid Strawberry”[1],some funding information was omitted.The funding DOI“10.13039/501100011033”was missing.T...In the article titled“Automatic Fruit Morphology Phenome and Genetic Analysis:An Application in the Octoploid Strawberry”[1],some funding information was omitted.The funding DOI“10.13039/501100011033”was missing.The corrected Acknowledgements section is provided below.展开更多
Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency.Among phenotypes,morphological traits are relevant in many fruit breeding programs,as appearance influences consumer ...Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency.Among phenotypes,morphological traits are relevant in many fruit breeding programs,as appearance influences consumer preference.Often,these traits are manually or semiautomatically obtained.Yet,fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose.Here,we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry(Fragaria x ananassa)images.The pipeline segments,classifies,and labels the images and extracts conformation features,including linear(area,perimeter,height,width,circularity,shape descriptor,ratio between height and width)and multivariate(Fourier elliptical components and Generalized Procrustes)statistics.Internal color patterns are obtained using an autoencoder to smooth out the image.In addition,we develop a variational autoencoder to automatically detect the most likely number of underlying shapes.Bayesian modeling is employed to estimate both additive and dominance effects for all traits.As expected,conformational traits are clearly heritable.Interestingly,dominance variance is higher than the additive component for most of the traits.Overall,we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable.Although we study strawberry images,the algorithm can be applied to other fruits,as shown in the GitHub repository.展开更多
文摘In the article titled“Automatic Fruit Morphology Phenome and Genetic Analysis:An Application in the Octoploid Strawberry”[1],some funding information was omitted.The funding DOI“10.13039/501100011033”was missing.The corrected Acknowledgements section is provided below.
基金the Planasa-IRTA collaboration contract,headed by AM.LMZ was supported by a PhD grant from the Ministry of Economy and Science(MINECO,Spain).Work was funded by the MINECO grants AGL2016-78709-R and PID2019-108829RB-I00 to MPEthe CERCA Programme/Generalitat de Catalunya.We acknowledge the financial support from the Spanish Ministry of Science and Innovation-State Research Agency(AEI),through the“Severo Ochoa Programme for Centres of Excellence in R&D”SEV-2015-0533 and CEX2019-000902-S.
文摘Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency.Among phenotypes,morphological traits are relevant in many fruit breeding programs,as appearance influences consumer preference.Often,these traits are manually or semiautomatically obtained.Yet,fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose.Here,we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry(Fragaria x ananassa)images.The pipeline segments,classifies,and labels the images and extracts conformation features,including linear(area,perimeter,height,width,circularity,shape descriptor,ratio between height and width)and multivariate(Fourier elliptical components and Generalized Procrustes)statistics.Internal color patterns are obtained using an autoencoder to smooth out the image.In addition,we develop a variational autoencoder to automatically detect the most likely number of underlying shapes.Bayesian modeling is employed to estimate both additive and dominance effects for all traits.As expected,conformational traits are clearly heritable.Interestingly,dominance variance is higher than the additive component for most of the traits.Overall,we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable.Although we study strawberry images,the algorithm can be applied to other fruits,as shown in the GitHub repository.
