ResNet101-SVM: Hybrid convolutional neural network for citrus fruits classification

Abstract

Recognizing and classifying citrus fruits is a challenging yet crucial task for agriculture, food processing, and quality control. Classifying citrus fruits is challenging because of their wide variety, often with a similar flesh appearance, shape, and size. Therefore, efficient and effective approaches are required for accurate identification. Our study focused on efficiently identifying citrus fruit types by utilizing a hybrid ResNet101-SVM model. ResNet101-SVM is the combination of the feature extraction capabilities of the ResNet101 with the classification power of SVM. This hybrid approach leverages the strengths of both deep learning (feature extraction) and traditional machine learning (SVM classification) to improve the accuracy and robustness of citrus fruit classification. The model outperformed the standard ResNet101 model across various performance metrics, achieving impressive accuracy, sensitivity, specificity, precision, F1 Score, MCC, and Kappa values of 99.81%, 99.81%, 99.8%, 99.82%, 0.18%, 99.81%, 99.80%, and 98.77%, respectively. This study holds significant promise for various applications, particularly in the domains of food processing and quality control.