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Classifying Wheat Genotypes using Machine Learning Models for Single Kernel Characterization System Measurements

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Title Classifying Wheat Genotypes using Machine Learning Models for Single Kernel Characterization System Measurements
 
Creator Kutlu, Imren
Gulmezoglu, Mehmet Bilginer
Karaduman, Yasar
 
Subject Common vector approach
Kernel hardness
KNN method
SVM classification
Wheat quality
 
Description 985-991
The properties related to market value, milling, classification, storage, and transportation of bread wheat are determined by using some important physical quality characteristics such as weight, shape, dimensions, and hardness of wheat kernels. It is possible to measure all these features using single kernel characterization system (SKCS). Classification of wheat genotypes using computer-based algorithms is crucial to determine the most accurate physical quality classification for breeding studies. In this paper, four commercial wheat cultivars (Altay-2000, Bezostaja-1, Harmankaya-99, and Kate A-1) and six doubled haploid (DH) wheat genotypes are studied to classify wheat cultivars and DH wheat genotypes separately. In the classification stage, feature vectors constructed from measured characters namely, kernel weight, diameter, hardness, and moisture are applied to well-known classifiers such as Common Vector Approach (CVA), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). Satisfactory results especially for the training set are obtained from the experimental studies. Classification results are compared with single linkage hierarchical cluster (SLHC) analysis, which is the most widely used in breeding studies. Recognition of clustered genotypes in all three classification methods and dendrograms present similar results. The SVM model is found to be outperformed over other methods for studied characters and could therefore effectively be utilized for characterizing, classifying and/or identifying the wheat genotypes.
 
Date 2021-11-17T06:05:22Z
2021-11-17T06:06:25Z
2021-11-17T06:05:22Z
2021-11-17T06:06:25Z
2021-11
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58524
 
Language en
 
Publisher CSIR-NIScPR
 
Source JSIR Vol.80(11) [November 2021]