Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network

被引:0
|
作者
Bankin, Mikhail [1 ]
Tyrykin, Yaroslav [1 ]
Duk, Maria [1 ]
Samsonova, Maria [1 ]
Kozlov, Konstantin [1 ]
机构
[1] Peter Great St Petersburg Polytech Univ, PhysMech Inst, Math Biol & Bioinformat Lab, St Petersburg 195251, Russia
来源
PLANTS-BASEL | 2024年 / 13卷 / 17期
关键词
artificial image objects; climatic factors; genomic prediction; chickpea; GWAS; convolutional neural network; GENE FAMILY; ARABIDOPSIS; PROTEIN; TRANSPORTERS; PREDICTION; COMPLEX; MEMBERS; PLANT;
D O I
10.3390/plants13172444
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
The chickpea plays a significant role in global agriculture and occupies an increasing share in the human diet. The main aim of the research was to develop a model for the prediction of two chickpea productivity traits in the available dataset. Genomic data for accessions were encoded in Artificial Image Objects, and a model for the thousand-seed weight (TSW) and number of seeds per plant (SNpP) prediction was constructed using a Convolutional Neural Network, dictionary learning and sparse coding for feature extraction, and extreme gradient boosting for regression. The model was capable of predicting both traits with an acceptable accuracy of 84-85%. The most important factors for model solution were identified using the dense regression attention maps method. The SNPs important for the SNpP and TSW traits were found in 34 and 49 genes, respectively. Genomic prediction with a constructed model can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired phenotype.
引用
收藏
页数:16
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