On to the Next Chapter for Crop Breeding: Convergence with Data Science

Ersoz, Elhan S.; Martín, Nicolas F.; Stapleton, Ann E.

doi:10.20944/preprints201903.0115.v1

Cited by 6 publications

(10 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thanks to the ever-increasing data generated by industry, farmers, and scholars, GS is expected to improve efficiency and help make specific breeding decisions. For this reason, a wide range of analytical methods, such as machine learning, deep learning, and artificial intelligence, are now being adapted for application in plant breeding to support analytics and decision-making processes [ 91 ].…”

Section: Main Bodymentioning

confidence: 99%

A review of deep learning applications for genomic selection

et al. 2021

View full text Add to dashboard Cite

Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

show abstract

Section: Main Bodymentioning

confidence: 99%

A review of deep learning applications for genomic selection

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Further work seems required to achieve similar improvements in plant populations and to compare DL to random-effects models designed to deal with dominance and epistasis ( Ramstein et al, 2020 ). Finally, ML can improve plant breeding; ( Ersoz et al., 2020 ) compared mixed model and ML approaches at several stages of breeding programs.…”

Section: Machine Learning For Genomic Predictionmentioning

confidence: 99%

Machine learning in plant science and plant breeding

et al. 2021

View full text Add to dashboard Cite

Summary Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

show abstract

“…Time to Maturity (TTM) is associated with the biological length cycle of a cultivar (Ersoz, Martin, and Stapleton 2020). Therefore, increasing the understanding of the factors that influence it is critical to define the geographical adaptation for new cultivars.…”

Section: Introductionmentioning

confidence: 99%

Development of a Soybean Maturity Prediction Model for Soybean Grown in African Environments

Marcillo

Diers

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Time-to-maturity (TTM) is an important trait in soybean breeding programs. However, soybean is a relatively new crop in Africa. As such, TTM information is not yet well defined as in other major producing areas. Multi Environment trials (MET) allow breeders to analyze crop performance across diverse conditions but also pose statistical challenges (e.g. unbalanced data). Modern statistical methods, e.g.. Generalized Additive Models (GAM), can flexibly smooth a range of responses while retaining observations that could be lost under other approaches. We leveraged 5 years of data from a MET breeding program in Africa to identify the best geographical and seasonal variables to explain site and genotypic differences in soybean TTM. Using soybean-cycle features (minimum temperature, daylength) along with trial geolocation (longitude, latitude), a GAM model predicted soybean TTM within 10 days of the average observed TTM [x = 105 days post- planting]. Further, we found significant differences between cultivars (p<0.05) in TTM sensitivity to minimum temperature and daylength. Our results show promise to advance the design of maturity systems that enhance soybean planting and breeding decisions in Africa.

show abstract

On to the Next Chapter for Crop Breeding: Convergence with Data Science

Cited by 6 publications

References 55 publications

A review of deep learning applications for genomic selection

A review of deep learning applications for genomic selection

Machine learning in plant science and plant breeding

Development of a Soybean Maturity Prediction Model for Soybean Grown in African Environments

Contact Info

Product

Resources

About