وصف البحث
This paper applies deep convolution neural networks (DCNN) to apple fruit
disease classification. Twelve DCNN methods (SqueezeNet, GoogleNet,
InceptionV3, DenseNet201, ReaNet50, ResNet101, Xception,
InceptionResnetV2, EfficientnetB0, AlexNet, VGG16, and VGG19) have
been used. These methods have been trained to classify apples into four
categories: normal, blotch, rot, and scab. A dataset of 5179 images,
including 3472 for normal, 171 for blotch, 1166 for rot, and 370 for scab,
has been used. A practical test on 120 images (30 for each category) has
been applied. Seven of these DCNNs—InceptionV3, DenseNet201,
ResNet101, ResNet50, GoogleNet, AlexNet, and VGG16—have the best
accuracy. InceptionV3 is the highest. It has achieved an accuracy of 100%
for all categories. The used dataset is unbalanced and small. So, it's
necessary to use data augmentation to overcome any overfitting that may
cause. After applying data augmentation, the dataset is balanced and
contains 13888 images (3472 for each category). The seven DCNNs are
retrained by the balanced dataset and retested by the same 120 images. All
DCNN's accuracy has enhanced except InceptionV3, which has decreased.
On the other hand, RasNet101 has achieved an accuracy of 100% for all
categories. Therefore, ResNet101 has been recommended for apple fruit
disease classification.
