TY  - JOUR
T1  - Görüntü Dönüştürücü Tabanlı Meta-Öğrenme ile Gürbüz Bir Elma Hastalığı Sınıflandırma Yaklaşımı
TT  - A Robust Apple Disease Classification Approach Using Vision Transformer-Based Meta-Learning
AU  - Gunde, Metehan
AU  - Işık, Gültekin
PY  - 2025
DA  - November
Y2  - 2025
DO  - 10.21597/jist.1641204
JF  - Journal of the Institute of Science and Technology
JO  - J. Inst. Sci. and Tech.
PB  - Iğdır Üniversitesi
WT  - DergiPark
SN  - 2536-4618
SP  - 1193
EP  - 1205
VL  - 15
IS  - 4
LA  - tr
AB  - Az örnekle öğrenme, etiketlenmiş verilerin kısıtlı olduğu tarımsal sınıflandırma problemleri için önemli bir çözüm olarak ortaya çıkmıştır. Bu çalışma elma yapraklarındaki hastalıkları sınıflandırmak amacıyla Görüntü Dönüştürücü modeli ile Model-Bağımsız Meta-Öğrenme algoritmalarını (MAML ve MAML++) birlikte kullanmayı önermektedir. Bu yaklaşımların performansı, 2-way 5-shot ve 4-way 1-shot senaryolarında değerlendirilmiştir. MAML algoritması, 2-way 5-shot senaryosunda en iyi performansını (96,27% doğruluk, 0,1299 kayıp) elde ederken, 4-way 1-shot senaryosunda en kötü performansını (88,80% doğruluk, 0,2884 kayıp) sergilemiş ve bu da %7,47'lik bir doğruluk farkı yaratmıştır. Buna karşılık, MAML++ algoritması, en iyi (90,73% doğruluk, 0,3580 kayıp) ve en kötü (83,60% doğruluk, 0,5401 kayıp) performansları arasında %7,13'lük daha küçük bir doğruluk farkı ile daha iyi bir tutarlılık sergilemiştir. Bu bulgular, MAML'ın daha iyi sınıflandırma performansına rağmen MAML++'ın daha tutarlı ve daha dayanıklı olduğunu göstermektedir. Bu çalışma, Görüntü Dönüştürücü modellerinin özellik çıkarım yeteneklerini meta-öğrenme algoritmalarıyla birleştirerek gerçek dünya koşullarında doğru ve güvenilir hastalık sınıflandırması için yeni bir yaklaşım sunmaktadır. Sonuçlar, geliştirilen yaklaşımın tarımsal uygulamalarda, özellikle etiketlenmiş verilerin kısıtlı olduğu senaryolarda önemli bir potansiyele sahip olduğunu göstermektedir.
KW  - Az sayıda örnekle öğrenme
KW  - Meta öğrenme
KW  - MAML
KW  - Görüntü Dönüştürücü
KW  - Görüntü sınıflandırma
KW  - Derin öğrenme
N2  - Few-shot learning has emerged as an important solution for agricultural classification problems, where labelled data is often scarce. This study proposes the integration of the Vision Transformer (ViT) model with Model-Agnostic Meta-Learning (MAML and MAML++) algorithms to classify apple leaf diseases. The performance of these approaches was evaluated in 2-way 5-shot and 4-way 1-shot scenarios. The MAML algorithm achieved its best performance in the 2-way 5-shot scenario (96.27% accuracy, 0.1299 loss), while its worst performance was observed in the 4-way 1-shot scenario (88.80% accuracy, 0.2884 loss), resulting in a 7.47% accuracy gap. In contrast, the MAML++ algorithm demonstrated better consistency, with a smaller accuracy gap of 7.13% between its best (90.73% accuracy, 0.3580 loss) and worst (83.60% accuracy, 0.5401 loss) performances. These findings indicate that, despite MAML achieving better classification performance, MAML++ is more consistent and robust. By combining the feature extraction capabilities of Vision Transformers with meta-learning algorithms, this study presents a new approach for accurate and reliable disease classification under real-world conditions. The results demonstrate that the proposed approach has significant potential in agricultural applications, particularly in scenarios with limited labelled data.
CR  - Akhtar, F., Partheeban, N., Daniel, A., Sriramulu, S., Mehra, S., & Gupta, N. (2021). Plant Disease Detection based on Deep Learning Approach. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2021, 74–77. https://doi.org/10.1109/ICACITE51222.2021.9404647
CR  - Andrew, J., Eunice, J., Popescu, D. E., Chowdary, M. K., & Hemanth, J. (2022). Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications. Agronomy, 12(10). https://doi.org/10.3390/agronomy12102395
CR  - Ball, J. (2021). Few-Shot Learning for Image Classification of Common Flora. http://arxiv.org/abs/2105.03056
Bracino, A. A., Concepcion, R. S., Bedruz, R. A. R., Dadios, E. P., & Vicerra, R. R. P. (2020). Development of a Hybrid Machine Learning Model for Apple (Malus domestica) Health Detection and Disease Classification. 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 1–6. https://doi.org/10.1109/HNICEM51456.2020.9400139
CR  - Chakraborty, S., Paul, S., & Rahat-Uz-Zaman, M. (2021). Prediction of Apple Leaf Diseases Using Multiclass Support Vector Machine. 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 147–151. https://doi.org/10.1109/ICREST51555.2021.9331132
CR  - Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR 2021 - 9th International Conference on Learning Representations. https://arxiv.org/abs/2010.11929v2
CR  - Figueroa-Flores, C., & San-Martin, P. (2023). Few-Shot Learning for Image Classification of Common Flora. ArXiv, abs/2105.03056. https://doi.org/10.3389/FPLS.2023.1211490
Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. 34th International Conference on Machine Learning, ICML 2017, 3, 1856–1868. https://arxiv.org/abs/1703.03400v3
Hughes, David. P., & Salathe, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. https://arxiv.org/abs/1511.08060v2
CR  - Işık, G. (2023). Improving Plant Disease Recognition Through Gradient-Based Few-shot Learning with Attention Mechanisms. Journal of the Institute of Science and Technology, 13(3), 1482–1495. https://doi.org/10.21597/JIST.1283491
CR  - Işık, G., & Paçal, İ. (2024). Few-shot classification of ultrasound breast cancer images using meta-learning algorithms. Neural Computing and Applications, 36(20), 12047–12059. https://doi.org/10.1007/S00521-024-09767-Y/TABLES/7
CR  - Liu, J., & Wang, X. (2021). Plant diseases and pests detection based on deep learning: a review. Plant Methods, 17(1). https://doi.org/10.1186/S13007-021-00722-9
CR  - Nagaraju, M., & Chawla, P. (2020). Systematic review of deep learning techniques in plant disease detection. International Journal of System Assurance Engineering and Management, 11(3), 547–560. https://doi.org/10.1007/S13198-020-00972-1/FIGURES/9
CR  - Nagaraju, Y., Venkatesh, Swetha, S., & Stalin, S. (2020). Apple and Grape Leaf Diseases Classification using Transfer Learning via Fine-tuned Classifier. 2020 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), 1–6. https://doi.org/10.1109/ICMLANT50963.2020.9355991
CR  - Nurgazin, M., & Tu, N. A. (2023). A Comparative Study of Vision Transformer Encoders and Few-shot Learning for Medical Image Classification. 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2505–2513. https://doi.org/10.1109/ICCVW60793.2023.00265
CR  - Pacal, I., & Işık, G. (2024). Utilizing convolutional neural networks and vision transformers for precise corn leaf disease identification. Neural Computing and Applications, 37(4), 2479–2496. https://doi.org/10.1007/S00521-024-10769-Z/TABLES/5
CR  - Ravi, S., & Larochelle, H. (2022). Optimization as a Model for Few-Shot Learning.
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant Disease Detection and Classification by Deep Learning. Plants 2019, Vol. 8, Page 468, 8(11), 468. https://doi.org/10.3390/PLANTS8110468
CR  - Shao, Y., Wu, W., You, X., Gao, C., & Sang, N. (2023). Improving the Generalization of MAML in Few-Shot Classification via Bi-Level Constraint. IEEE Transactions on Circuits and Systems for Video Technology, 33(7), 3284–3295. https://doi.org/10.1109/TCSVT.2022.3232717
CR  - Thapa, R., Zhang, K., Snavely, N., Belongie, S., & Khan, A. (2020). The Plant Pathology Challenge 2020 data set to classify foliar disease of apples. Applications in Plant Sciences, 8(9). https://doi.org/10.1002/APS3.11390
CR  - Wu, X., Deng, H., Wang, Q., Lei, L., Gao, Y., & Hao, G. (2023). Meta-learning shows great potential in plant disease recognition under few available samples. The Plant Journal, 114(4), 767–782. https://doi.org/10.1111/TPJ.16176
CR  - Xu, J., & Du, Q. (2020). Learning transferable features in meta-learning for few-shot text classification. Pattern Recognit. Lett., 135, 271–278. https://doi.org/10.1016/J.PATREC.2020.05.007
CR  - Vezıroglu, E., Pacal, I., & Coşkunçay, A. (2023). Derin Evrişimli Sinir Ağları Kullanılarak Pirinç Hastalıklarının Sınıflandırılması. Journal of the Institute of Science and Technology, 13(2), 792-814. 
https://doi.org/10.21597/jist.1265769
CR  - Ye, H. J., Ming, L., Zhan, D. C., & Chao, W. L. (2024). How to Train Your MAML to Excel in Few-Shot Classification. ArXiv, abs/2106.16245(3), 1425–1440. https://doi.org/10.1109/TPAMI.2022.3160362
UR  - https://doi.org/10.21597/jist.1641204
L1  - https://dergipark.org.tr/tr/download/article-file/4615617
ER  -