Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest

被引:2
|
作者
Bavykina, Maria [1 ]
Kostina, Nadezhda [1 ]
Lee, Cheng-Ruei [2 ]
Schafleitner, Roland [3 ]
Bishop-von Wettberg, Eric [4 ]
Nuzhdin, Sergey V. [1 ,5 ]
Samsonova, Maria [1 ]
Gursky, Vitaly [6 ]
Kozlov, Konstantin [1 ]
机构
[1] Peter Great St Petersburg Polytech Univ, Math Biol & Bioinformat Lab, St Petersburg 195251, Russia
[2] Natl Taiwan Univ, Inst Ecol & Evolutionary Biol, Taipei 106319, Taiwan
[3] World Vegetable Ctr, Tainan 74151, Taiwan
[4] Univ Vermont, Gund Inst Environm, Dept Plant & Soil Sci, Burlington, VT 05405 USA
[5] Univ Calif Los Angeles, Program Mol & Computat Biol, Los Angeles, CA 90095 USA
[6] Ioffe Inst, Theoret Dept, St Petersburg 194021, Russia
来源
PLANTS-BASEL | 2022年 / 11卷 / 23期
关键词
flowering time; mung bean; artificial image objects; climatic factors; GWAS; convolutional neural network; random forest; GENOME-WIDE ASSOCIATION; POPULATION STRATIFICATION; CROPS;
D O I
10.3390/plants11233327
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important features in a random forest model. The genotypic and weather data are encoded in artificial image objects, and a model for flowering time prediction is constructed as a convolutional neural network. The model uses weather data for only a limited time period of 5 days before and 20 days after planting and is capable of predicting the time to flowering with high accuracy. The most important factors for model solution were identified using saliency maps and a Score-CAM method. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time.
引用
收藏
页数:20
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