Comparison of convolutional neural network (CNN) methods using AlexNet and VGG16 architectures for disease detection in apples

Key Points

• The aim is to compare the effectiveness of AlexNet and VGG16 convolutional neural networks for detecting diseases in apples.
• Implemented two CNN architectures: AlexNet and VGG16.
• Used a dataset of apple images to train and evaluate models.
• Conducted performance analysis based on accuracy and detection rates.
• AlexNet achieved a detection accuracy of 92% compared to 85% for VGG16.
• Reported faster processing time for AlexNet at 2 seconds per image versus 4 seconds for VGG16.
• Indicated that AlexNet outperforms VGG16 in identifying certain types of apple diseases.

Abstract