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Agriculture
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Machine Learning and Biological Data in Crop Genetics and Breeding
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Machine Learning and Biological Data in Crop Genetics and Breeding

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Genotype Evaluation and Breeding".

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 4648

Special Issue Editor

Dr. Shannon K. Dillon

E-Mail Website
Guest Editor
CSIRO Agriculture & Food, Building 801, Clunies Ross Street, Black Mountain, ACT 2601, Australia
Interests: plant genomics; machine learning; genomic prediction; genome/ transcriptome wide association; representation learning; bioinformatics

Special Issue Information

Dear Colleagues,

Over the past decade, machine learning (ML) has revolutionised economic forecasting, clinical diagnostics, and speech recognition. In recent years, widespread adoption of these techniques has been seen in agriculture, where progress in the areas of automated phenotyping, genome to phenome inference, and genomic prediction are contributing to the advancement of crop genetic diversity and its management worldwide. This has been underpinned by the increasing availability of rich biological data in crops, from high-resolution images and environmental sensors, to multi-‘omic data capturing relationships among molecular strata. The complexity of biological data and challenges of scale mean that the full potential of ML in crop genetics is still being realised. 

This Special Issue focuses on the current application of machine learning approaches for integration of diverse biological data, toward improving management of crop genetic resources for increased field performance and rates of genetic gain. This Special Issue will include interdisciplinary studies coupling ML with high resolution phenomics, genomics (multi-‘omics), controlled and field scale environmental data in agricultural crops that demonstrate progress in candidate gene identification, pre-breeding, and genomic prediction for a wide range of crop traits. All types of articles, such as original research, opinions, and reviews, are welcome.

Dr. Shannon K. Dillon
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • genomic prediction
  • genomic selection
  • genome wide association
  • crop breeding
  • crop improvement
  • phenomics
  • automated phenotyping
  • crop genetics

Published Papers (2 papers)

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Research

16 pages, 18349 KiB  
Article
Estimation of Error Variance in Genomic Selection for Ultrahigh Dimensional Data
by Sayanti Guha Majumdar, Anil Rai and Dwijesh Chandra Mishra
Agriculture 2023, 13(4), 826; https://doi.org/10.3390/agriculture13040826 - 04 Apr 2023
Cited by 1 | Viewed by 1020
Abstract
Estimation of error variance in the case of genomic selection is a necessary step to measure the accuracy of the genomic selection model. For genomic selection, whole-genome high-density marker data is used where the number of markers is always larger than the sample [...] Read more.
Estimation of error variance in the case of genomic selection is a necessary step to measure the accuracy of the genomic selection model. For genomic selection, whole-genome high-density marker data is used where the number of markers is always larger than the sample size. This makes it difficult to estimate the error variance because the ordinary least square estimation technique cannot be used in the case of datasets where the number of parameters is greater than the number of individuals (i.e., p > n). In this article, two existing methods, viz. Refitted Cross Validation (RCV) and kfold-RCV, were suggested for such cases. Moreover, by considering the limitations of the above methods, two new methods, viz. Bootstrap-RCV and Ensemble method, have been proposed. Furthermore, an R package “varEst” has been developed, which contains four different functions to implement these error variance estimation methods in the case of Least Absolute Shrinkage and Selection Operator (LASSO), Least Squares Regression (LSR) and Sparse Additive Models (SpAM). The performances of the algorithms have been evaluated using simulated and real datasets. Full article
(This article belongs to the Special Issue Machine Learning and Biological Data in Crop Genetics and Breeding)
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12 pages, 3369 KiB  
Article
Genomic Prediction of Wheat Grain Yield Using Machine Learning
by Manisha Sanjay Sirsat, Paula Rodrigues Oblessuc and Ricardo S. Ramiro
Agriculture 2022, 12(9), 1406; https://doi.org/10.3390/agriculture12091406 - 06 Sep 2022
Cited by 7 | Viewed by 3051
Abstract
Genomic Prediction (GP) is a powerful approach for inferring complex phenotypes from genetic markers. GP is critical for improving grain yield, particularly for staple crops such as wheat and rice, which are crucial to feeding the world. While machine learning (ML) models have [...] Read more.
Genomic Prediction (GP) is a powerful approach for inferring complex phenotypes from genetic markers. GP is critical for improving grain yield, particularly for staple crops such as wheat and rice, which are crucial to feeding the world. While machine learning (ML) models have recently started to be applied in GP, it is often unclear what are the best algorithms and how their results are affected by the feature selection (FS) methods. Here, we compared ML and deep learning (DL) algorithms with classical Bayesian approaches, across a range of different FS methods, for their performance in predicting wheat grain yield (in three datasets). Model performance was generally more affected by the prediction algorithm than the FS method. Among all models, the best performance was obtained for tree-based ML methods (random forests and gradient boosting) and for classical Bayesian methods. However, the latter was prone to fitting problems. This issue was also observed for models developed with features selected by BayesA, the only Bayesian FS method used here. Nonetheless, the three other FS methods led to models with no fitting problem but similar performance. Thus, our results indicate that the choice of prediction algorithm is more important than the choice of FS method for developing highly predictive models. Moreover, we concluded that random forests and gradient boosting algorithms generate highly predictive and robust wheat grain yield GP models. Full article
(This article belongs to the Special Issue Machine Learning and Biological Data in Crop Genetics and Breeding)
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