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Year 2024, Volume: 45 Issue: 4, 701 - 706, 30.12.2024

Esra Kavalcı Yılmaz , Hatice Aktaş , Kemal Adem

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

Abstract

References

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  • 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)
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  • [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)
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  • [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.
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  • [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).

Classification of Grapevine Leaf Types with Vision Transformer Architecture

Year 2024, Volume: 45 Issue: 4, 701 - 706, 30.12.2024

Esra Kavalcı Yılmaz , Hatice Aktaş , Kemal Adem

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

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).
There are 36 citations in total.

Details

Primary Language English
Subjects Plant Biotechnology
Journal Section Natural Sciences
Authors

Esra Kavalcı Yılmaz 0000-0003-1314-4495

Hatice Aktaş 0000-0002-1104-9307

Kemal Adem 0000-0002-3752-7354

Publication Date December 30, 2024
Submission Date September 11, 2024
Acceptance Date December 13, 2024
Published in Issue Year 2024Volume: 45 Issue: 4

Cite

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
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