Implementation and Assessment of New Hybrid Model for Cashew Kernel Classification
Keywords:
Convolutional Neural Network, SVM, RF, Custom, KNN, Machine LearningAbstract
Convolutional Neural Networks, represents a cutting-edge methodology for image classification. Numerous CNN models have been effectively deployed for classification of images. The training of CNN involves leveraging sophisticated deep learning algorithms, leading to significant milestones in large-scale identification methods within the realm of machine learning. Cashew nuts are widely consumed worldwide and are classified into different grades based on their size and quality. The current manual sorting and grading process for cashew kernels is labor-intensive and time-consuming. This paper proposes 9 hybrid models for cashew classification such as VGG16 + SVM, VGG16 + RF, VGG16 + KNN, Inception-V3 + SVM, Inception-V3 + RF, Inception-V3 ,VGG16 + SVM, VGG16 + RF, VGG16 + KNN, Inception-V3 + SVM, Inception-V3 + RF, Inception-V3 + KNN, ResNet50 + SVM, ResNet50 + RF, ResNet50 + KNN, Custom + SVM, were implemented. The results revealed that the ResNet-50 model combined with SVM gave highest accuracy of 97.40%. The results obtained in this paper indicate that the fusion of convolutional neural networks (CNNs) and classifiers in hybrid models yields significant improvements in automated cashew grading.
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