期刊
PLANT PHENOMICS
卷 2021, 期 -, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.34133/2021/9812910
关键词
-
资金
- Ministry of Economy and Science (MINECO, Spain)
- CERCA Programme/Generalitat de Catalunya
- Spanish Ministry of Science and Innovation-State Research Agency (AEI), through the Severo Ochoa Programme for Centres of Excellence in RD [SEV-2015-0533, CEX2019000902-S]
- MINECO [AGL2016-78709-R, PID2019-108829RB-I00]
Automatizing phenotype measurement can significantly improve plant breeding efficiency. This study presents an automated pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from fruit images. The results demonstrate that conformational traits are heritable, with dominance variance exceeding additive components, and fruit shape and color can be quickly and automatically evaluated.
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.
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