Evaluation of Cultivar Identification Performance Using Feature Expressions and Classification Algorithms on Optical Images of Sweet Corn Seeds

• Main Authors: Yu Tang, Zhishang Cheng, Aimin Miao, Jiajun Zhuang, Chaojun Hou, Yong He, Xuan Chu, Shaoming Luo
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Agronomy
• Subjects: sweet corn seed; cultivar classification; optical image; performance evaluation; separability criterion
• Online Access: https://www.mdpi.com/2073-4395/10/9/1268

Cultivar identification of seeds is important for crop yield and quality. To study the impact of different features expressions and classification methods on cultivar identification, the performance of the feature expressions and classification algorithms affecting the accuracy of cultivar identification was evaluated by image processing techniques. A total of 448 samples of seeds from seven cultivars of sweet corn, namely, Orlando, Beiyasi, Jingketian 183, Jingtian 218, Suitian 1, CT76 and Lilixiangtian, were evaluated. The color, shape and texture features of the seeds were extracted from the images, and the class separability criterion was adopted to evaluate the separability of the features of the embryo side, nonembryo side and both of them combined. The results indicate that the class separability based on the features of the embryo side was higher than that based on the nonembryo side and both of them combined. Based on the embryo-side optical feature data, dimensionality reduction was conducted by two feature selection methods (stepwise discriminant analysis (SDA) and genetic algorithm (GA)) and two feature extraction methods (principal component analysis (PCA) and kernel principal component analysis (KPCA)). Performance evaluation of the feature reductions was conducted by constructing <i>k</i>-nearest neighbor (<i>K</i>-NN), naïve Bayes (NB), linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Compared to the PCA and KPCA algorithms, the SDA and GA algorithms were more conducive to the cultivar classification of sweet corn seeds; the critical features selected specifically by the SDA, <i>K</i>-NN, NB, LDA and SVM classifiers achieved the best classification accuracies (81.43%, 82.86%, 90%, and 87.14%, respectively). Analysis of variance (ANOVA) revealed that the approach for optical feature selection had a more significant effect on the identification of sweet corn seed cultivars than did the classifiers. Therefore, based on the optical images of the embryo side and the key features obtained by the feature selection method, a classification model was constructed for the accurate and nondestructive classification of different sweet corn seed cultivars.