Manuscript received September 25, 2022; revised November 16, 2022; accepted November 22, 2022.
Abstract—Grapevine leaves are utilized worldwide in a vast
range of traditional cuisines. As their price and flavor differ
from kind to kind, recognizing various species of grapevine
leaves is becoming an essential task. In addition, the
differentiation between grapevine leaf types by human sense
is difficult and time-consuming. Thus, building a machine
learning model to automate the grapevine leaf classification
is highly beneficial. Therefore, this is the primary focus of
this work. This paper uses a CNN-based model to classify
grape leaves by adapting DenseNet201. This study
investigates the impact of layer freezing on the performance
of DenseNet201 throughout the fine-tuning process. This
work used a public dataset consist of 500 images with 5
different classes (100 images per class). Several data
augmentation methods used to expand the training set. The
proposed CNN model, named DenseNet-30, outperformed
the existing grape leaf classification work that the dataset
borrowed from by achieving 98% overall accuracy.
Keywords—grapevine leaves varieties, pre-trained CNN,
fine-tuning, layer freezing, DenseNet201
Cite: Hunar A. Ahmed, Hersh M. Hama, Shayan I. Jalal, and Mohammed H. Ahmed*, "Deep Learning in Grapevine Leaves Varieties Classification Based on Dense Convolutional Network," Journal of Image and Graphics, Vol. 11, No. 1, pp. 98-103, March 2023.
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