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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