A Comparative Study for Fruit Classification Using Different CNN Machines
DOI:
https://doi.org/10.31272/jeasd.3790الكلمات المفتاحية:
AlexNet، CNN، Fruit classification، Machine learning، VGG16الملخص
The automation of fruit categorization and recognition is gaining popularity, yet it is becoming increasingly challenging due to low contrast and ambiguous features. Nonetheless, autonomous fruit categorization is a complex process reliant on the locations, shapes, colors, and sizes of the objects. In this paper, five Convolutional Neural Network (CNN) models for fruit classification are evaluated to determine the best-performing model. These models are: AlexNet, VGG16, VGG19, ResNet50, and GoogleNet. The architecture layers of the deep learning methods are presented, including convolutional, pooling, and fully connected layers. To evaluate different measures of the model's performance, the confusion matrix is applied. The performance of the CNN models is evaluated on a dataset of 1000 images, with 200 images per category. The results show that VGG16 achieves the best performance among the models used in these investigations. The VGG16 model has achieved an accuracy of 0.901, an error rate of 0.099, and 0.9 for all of (precision, recall, and F-measure, respectively).
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