2022
DOI: 10.3389/fgene.2022.822173
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Plant Genotype to Phenotype Prediction Using Machine Learning

Abstract: Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to auto… Show more

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Cited by 42 publications
(26 citation statements)
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“…of phenotypic data collection, high-throughput techniques and phenotype platforms are needed (Araus & Cairns, 2014;Danilevicz et al, 2022).…”
Section: Crop Sciencementioning
confidence: 99%
“…of phenotypic data collection, high-throughput techniques and phenotype platforms are needed (Araus & Cairns, 2014;Danilevicz et al, 2022).…”
Section: Crop Sciencementioning
confidence: 99%
“…Similarly, Ma et al., 2018 ., successfully developed a ML model to predict eight phenotypic traits among 2000 wheat individuals using 33,709 DArT (Diversity Array Technology) markers ( Ma et al., 2018 ). ML is now also being used to predict mature yield in early development using a combination of image and genotype data ( Danilevicz et al., 2021 ; Danilevicz et al., 2022 ). Recently ML models were developed for identification of core and dispensable genes in Oryza sativa L. and Brachypodium distachyon (L.) P. Beauv.…”
Section: Machine Learning and Cwrsmentioning
confidence: 99%
“…100 Diverse ML approaches for precise disease recognition and prediction have been implemented in plant populations. 101,102 Neural networks (NNs) have achieved impressive results in plant disease prediction using image classification. A deep convolutional network was implemented in leaf image classification model for disease recognition.…”
Section: Phenomicsmentioning
confidence: 99%