Classification of Grapevine Leaf Types with Vision Transformer Architecture

Esra Kavalcı Yılmaz; Hatice Aktaş; Kemal Adem

doi:10.17776/csj.1548189

EN

Classification of Grapevine Leaf Types with Vision Transformer Architecture

Abstract

Viticulture plays an important role in agriculture. Farmers prefer grapevine cultivation because not only its fruit but also its leaves are used in various fields. Both the use and trade of grapevine leaves within the country is an important source of income. Grapevine leaves, which are grown in almost all countries and used as edible, vary in terms of species. Determining and cultivating the species according to their suitability in terms of productivity is important. In this study, artificial intelligence methods were used to classify grapevine leaf species. The dataset consisting of five different classes, including 100 grapevine leaf images for each class, totalling 500 images, was classified using ViT, VGG19 and MobileNet methods. When the methods used in this study to help increase productivity in production are evaluated, ViT method has the best accuracy rate with 94%.

Keywords

Grapevine leaf classification, deep learning, ViT, artificial intelligence

References

[1] Gülcü M., Torçuk A. İ., Yemeklik Asma Yaprağı Üretimi ve Pazarlamasında Kalite Parametreleri, Meyve Bilimi, c. 1, ss. 75-79, (2016)
2] Adem K., Yılmaz E. K., Ölmez F., Çelik K., Bakır H., A Comparative Analysis of Deep Learning Parameters for Enhanced Detection of Yellow Rust in Wheat, UMAG, 16(2) (2024)
[3] Yılmaz E. K., Oğuz T., Adem K., A CNN-Based Hybrid Approach to Classification of Raisin Grains, 1st International Conference on Frontiers in Academic Research, (2023).
[4] Yılmaz E. K., Adem K., Kılıçarslan S., Aydın H. A., Classification of lemon quality using hybrid model based on Stacked AutoEncoder and convolutional neural network, Eur Food Res Technol, 249(6) (2023) 1655-1667.
[5] Hernández I., Gutiérrez S., Ceballos S., Iñíguez R., Barrio I., Tardaguila J., Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine, Horticulturae, 7(5) (2021)
[6] Cruz A. vd., Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence, Computers and Electronics in Agriculture, 157 (2019) 63-76.
[7] Poblete-Echeverría C., Hernández I., Gutiérrez S., Iñiguez R., Barrio I., Tardaguila J., Using artificial intelligence (AI) for grapevine disease detection based on images, BIO Web Conf., 68 (2023) 01021.
[8] Nagi R. Tripathy S. S., Deep convolutional neural network based disease identification in grapevine leaf images, Multimed Tools Appl, 81 (18) (2022) 24995-25006.
[9] Alessandrini M., Calero Fuentes Rivera R., Falaschetti L., Pau D., Tomaselli V., ve Turchetti C., A grapevine leaves dataset for early detection and classification of esca disease in vineyards through machine learning, Data in Brief, 35 (2021) 106809.
[10] Jaisakthi S. M., Mirunalini P., Thenmozhi D., Vatsala, Grape Leaf Disease Identification using Machine Learning Techniques, 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), (2019) 1-6.

[11] Moghimi A., Pourreza A., Zuniga-Ramirez G., Williams L. E., Fidelibus M. W., A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery, Remote Sensing, 12(21) (2020) 3515.
[12] İmak A., Doğan G., Şengür A., ve Ergen B., Asma Yaprağı Türünün Sınıflandırılması için Doğal ve Sentetik Verilerden Derin Öznitelikler Çıkarma, Birleştirme ve Seçmeye Dayalı Yeni Bir Yöntem, International Journal of Pure and Applied Sciences, 9(1) (2023) 46-55.
[13] Koklu M., Unlersen M. F., Ozkan I. A., Aslan M. F., Sabanci K., A CNN-SVM study based on selected deep features for grapevine leaves classification, Measurement, 188 (2022) 110425.
[14] Zhang, L., Wen, Y. A transformer-based framework for automatic COVID19 diagnosis in chest CTs. In Proceedings of the IEEE/CVF international conference on computer vision, (2021) 513-518.
[15] Tyagi, K., Pathak, G., Nijhawan, R., & Mittal, A. Detecting pneumonia using vision transformer and comparing with other techniques. In 2021 5th international conference on electronics, communication and aerospace technology (ICECA), (2021) 12-16.
[16] Dai, Y., Gao, Y., Liu, F., Transmed: Transformers advance multi-modal medical image classification, Diagnostics, 11(8) (2021) 1384.
[17] Kamran, S. A., Hossain, K. F., Tavakkoli, A., Zuckerbrod, S. L., Baker, S. A., Vtgan: Semi-supervised retinal image synthesis and disease prediction using vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, (2021)3235-3245.
[18] Zeid, M. A. E., El-Bahnasy, K., Abo-Youssef, S. E., Multiclass colorectal cancer histology images classification using vision transformers. In 2021 tenth international conference on intelligent computing and information systems (ICICIS) (2021) 224-230.
[19] Xu, X., Guan, Y., Li, J., Ma, Z., Zhang, L., Li, L., Automatic glaucoma detection based on transfer induced attention network, BioMedical Engineering OnLine, 20(1) (2021) 39.
[20] Grapevine Leaves Image Dataset, https://www.kaggle.com/datasets/muratkokludataset/grapevine-leaves-image-dataset
[21] Alaca Y., Emin B., Akgul A., A comparative study of deep learning models and classification algorithms for chemical compound identification and Tox21 prediction, Computers & Chemical Engineering, 189 (2024) 108805,
[22] Assim O. M., Mahmood A. F., A novel Universal Deep Learning Approach for Accurate Detection of Epilepsy, Medical Engineering & Physics, (2024) 104219.
[23] Közkurt C., Diker A., Elen A., Kılıçarslan S., Dönmez E., ve Demir F. B., Trish: an efficient activation function for CNN models and analysis of its effectiveness with optimizers in diagnosing glaucoma, J Supercomput, 80 (11) (2024) 15485-15516.
[24] Kılıçarslan S., Diker A., Közkurt C., Dönmez E., Demir F. B., ve Elen A., Identification of multiclass tympanic membranes by using deep feature transfer learning and hyperparameter optimization, Measurement, c. 229, s. 114488, (2024)
[25] Daza A., González Rueda N. D., Aguilar Sánchez M. S., Robles Espíritu W. F., Chauca Quiñones M. E., Sentiment Analysis on E-Commerce Product Reviews Using Machine Learning and Deep Learning Algorithms: A Bibliometric Analysis, Systematic Literature Review, Challenges and Future Works, International Journal of Information Management Data Insights, 4 (2) (2024) 100267.
[26] Altaş Z., Ozguven M., Adem K., Bazı Bağ Hastalıklarının Faster R-CNN Modeli ile Otomatik Tespit Edilmesi ve Sınıflandırılması, Turkish Journal of Agriculture - Food Science and Technology, 11(97) 97-103.
[27] Adem K., Ozguven M. M., ve Altas Z., A sugar beet leaf disease classification method based on image processing and deep learning, Multimed Tools Appl, 82(8) (2023) 12577-12594,
[28] Alnasyan B., Basheri M., Alassafi M., The power of Deep Learning techniques for predicting student performance in Virtual Learning Environments: A systematic literature review, Computers and Education: Artificial Intelligence, 6 (2024) 100231.
[29] Dönmez E., Kılıçarslan S., ve Diker A., Classification of hazelnut varieties based on bigtransfer deep learning model, Eur Food Res Technol, c. 250, sy 5, ss. 1433-1442, (2024)
[30] Dosovitskiy A. vd., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, International Conference on Learning Representations, (2020)
[31] Dümen S., Yılmaz E. K., Adem K., ve Avaroglu E., Performance of vision transformer and swin transformer models for lemon quality classification in fruit juice factories, European Food Research and Technology, ss. 1-12, (2024)
[32] Heo, J., Seo, S., Kang, P., Exploring the differences in adversarial robustness between ViT-and CNN-based models using novel metrics, Computer Vision and Image Understanding, 235 (2023) 103800.
[33] Simonyan K. Zisserman A., Very Deep Convolutional Networks for Large-Scale Image Recognition, (2015) arXiv: arXiv:1409.1556.
[34] Bansal M., Kumar M., Sachdeva M., Mittal A., Transfer learning for image classification using VGG19: Caltech-101 image data set, J Ambient Intell Human Comput, 14(4) (2023) 3609-3620.
[35] Shome N., Kashyap R., Laskar R. H., Detection of tuberculosis using customized MobileNet and transfer learning from chest X-ray image, Image and Vision Computing, 147 (2024) 105063.
[36] Kılıçarslan S., Aydın H. A., Adem K., Yılmaz E. K., Impact of optimizers functions on detection of Melanoma using transfer learning architectures, Multimed Tools Appl (2024).

Details

Primary Language

English

Subjects

Plant Biotechnology

Journal Section

Research Article

Authors

Esra Kavalcı Yılmaz ^*
0000-0003-1314-4495
Türkiye

Hatice Aktaş
0000-0002-1104-9307
Türkiye

Kemal Adem
0000-0002-3752-7354
Türkiye

Publication Date

December 30, 2024

Submission Date

September 11, 2024

Acceptance Date

December 13, 2024

Published in Issue

Year 2024 Volume: 45 Number: 4

DOI

https://doi.org/10.17776/csj.1548189

IZ

https://izlik.org/JA44FK29KA

APA

Kavalcı Yılmaz, E., Aktaş, H., & Adem, K. (2024). Classification of Grapevine Leaf Types with Vision Transformer Architecture. Cumhuriyet Science Journal, 45(4), 701-706. https://doi.org/10.17776/csj.1548189

AMA

1.Kavalcı Yılmaz E, Aktaş H, Adem K. Classification of Grapevine Leaf Types with Vision Transformer Architecture. CSJ. 2024;45(4):701-706. doi:10.17776/csj.1548189

Chicago

Kavalcı Yılmaz, Esra, Hatice Aktaş, and Kemal Adem. 2024. “Classification of Grapevine Leaf Types With Vision Transformer Architecture”. Cumhuriyet Science Journal 45 (4): 701-6. https://doi.org/10.17776/csj.1548189.

EndNote

Kavalcı Yılmaz E, Aktaş H, Adem K (December 1, 2024) Classification of Grapevine Leaf Types with Vision Transformer Architecture. Cumhuriyet Science Journal 45 4 701–706.

IEEE

[1]E. Kavalcı Yılmaz, H. Aktaş, and K. Adem, “Classification of Grapevine Leaf Types with Vision Transformer Architecture”, CSJ, vol. 45, no. 4, pp. 701–706, Dec. 2024, doi: 10.17776/csj.1548189.

ISNAD

Kavalcı Yılmaz, Esra - Aktaş, Hatice - Adem, Kemal. “Classification of Grapevine Leaf Types With Vision Transformer Architecture”. Cumhuriyet Science Journal 45/4 (December 1, 2024): 701-706. https://doi.org/10.17776/csj.1548189.

JAMA

1.Kavalcı Yılmaz E, Aktaş H, Adem K. Classification of Grapevine Leaf Types with Vision Transformer Architecture. CSJ. 2024;45:701–706.

MLA

Kavalcı Yılmaz, Esra, et al. “Classification of Grapevine Leaf Types With Vision Transformer Architecture”. Cumhuriyet Science Journal, vol. 45, no. 4, Dec. 2024, pp. 701-6, doi:10.17776/csj.1548189.

Vancouver

1.Esra Kavalcı Yılmaz, Hatice Aktaş, Kemal Adem. Classification of Grapevine Leaf Types with Vision Transformer Architecture. CSJ. 2024 Dec. 1;45(4):701-6. doi:10.17776/csj.1548189

Cited By

A Hybrid Approach Based on ViT and Capsule Networks for Classification of Agricultural Plant Diseases

Applied Fruit Science

https://doi.org/10.1007/s10341-026-01780-w

BiKAN-ViT: Enhancing vision transformers via spline-based patch embedding and nonlinear token mixing

Applied Soft Computing

https://doi.org/10.1016/j.asoc.2026.115297

As of 2026, Cumhuriyet Science Journal will be published in six issues per year, released in February, April, June, August, October, and December