Evaluating CNN Models and Optimization Techniques for Quality Classification of Dried Chili Peppers (Capsicum annuum L.)

Author:

Lopez-Betancur DanielaORCID,Saucedo-Anaya TonatiuhORCID,Guerrero-Mendez CarlosORCID,Navarro-Solís DavidORCID,Silva-Acosta Luis,Robles-Guerrero AntonioORCID,Gomez-Jimenez SalvadorORCID

Abstract

This paper analyzes Convolutional Neural Network (CNN) models for classifying dried chili pepper quality. The models categorize images into five categories: “Extra”, “First Class”, “Second Class”, “Trash”, and “Empty”, each representing different qualities and scenarios in a sorting machine. We compared architectures from the Torchvision library, including ResNet, ResNeXt, Wide_ResNet, and RegNet using Transfer Learning (TL) in a feature extraction approach. All models employ residual blocks, an innovative technique enhancing deep learning performance. The models were evaluated using crossvalidation and metrics such as Precision, Recall, Specificity, F1-score, Geometric_mean, Index of Balanced Accuracy, and the Matthews Correlation Coefficient. They were trained using SGD, Adagrad, and Adam optimizers. Our findings suggest that ResNet-152, trained with the Adagrad optimizer, achieved the highest mean validation accuracy of 96.62%. The selected model can assist agricultural producers in classifying their products according to international standards.

Publisher

Editorial Académica Dragón Azteca

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