Abstract
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 k-nearest neighbor (K-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, K-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.
Highlights
Sweet corn (Zea mays var. saccharata) is a subspecies of maize whose milky stage is rich in sugar, various amino acids, vitamins, minerals and dietary fiber
In Abbaspourgilandeh et al (2020), the color, shape and texture features from the optical images of different rice cultivars with nonlinear relationship were extracted, and the rice cultivar classification model was established by discriminant analysis (DA) and artificial neural network (ANN); the results indicated that ANN achieved a better identification accuracy than that of DA [31]
The separability of the optical features of the embryo side and nonembryo side of sweet corn seeds was evaluated by a class separability criterion, and the results indicated that the class separability of the embryo side was higher than that of the nonembryo side and both of them combined
Summary
Sweet corn (Zea mays var. saccharata) is a subspecies of maize whose milky stage is rich in sugar, various amino acids, vitamins, minerals and dietary fiber. Based on its high nutritional and edible value [1,2,3], the economic benefit of sweet corn is twice that of ordinary corn. To meet the yield and quality requirements for crop production, the safety, high quality and reliability of seeds are important for planting. The sweet corns are generally harvested at milk-ripe stage, about 20–22 days after pollinaiton, and the immature sweet kernel of ear endosperm is the main product; pure seed is essential for uniform harvesting time, uniform maturity, appropriate shelf-life and timely consumption. To control seed quality and avoid repeat cultivation, rapid and accurate methods to measure the purity of sweet corn seed are highly important for industrial production of sweet corn
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