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

985-991The 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

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

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

Yield and Quality in Purple-Grained Wheat Isogenic Lines

Breeding programs for purple wheat are underway in many countries but there is a lack of information on the effects of Pp (purple pericarp) genes on agronomic and quality traits in variable environments and along the product chain (grain-flour-bread). This study was based on unique material: two pairs of isogenic lines in a spring wheat cv. Saratovskaya-29 (S29) background differing only in Pp genes and grain color. In 2017, seven experiments were conducted in Kazakhstan, Russia, and Turkey with a focus on genotype and environment interaction and, in 2018, one experiment in Turkey with a focus on grain, flour, and bread quality. The eect of environment was greater compared to genotype for the productivity and quality traits studied. Nevertheless, several important traits, such as grain color and anthocyanin content, are closely controlled by genotype, offering the opportunity for selection. Phenolic content in purple-grained lines was not significantly higher in whole wheat flour than in red-colored lines. However, this trait was significantly higher in bread. For antioxidant activities, no differences between the genotypes were detected in both experiments. Comparison of two sources of Pp genes demonstrated that the lines originating from cv. Purple Feed had substantially improved productivity and quality traits compared to those from cv. Purple