Implementation Of Hybrid EfficientNet V2 And Vision Transformer for Apple Leaf Diseases Classification

Authors

  • I Gede Susrama Mas Diyasa University of Pembangunan Nasional Veteran Jawa Timur
  • Surjo Hadi university of Pembangunan Nasional Veteran Jawa Timur
  • Budi Nugroho university of Pembangunan Nasional Veteran Jawa Timur
  • Sri Fuji Santoso university of Pembangunan Nasional Veteran Jawa Timur

DOI:

https://doi.org/10.33005/itij.v3i1.42

Keywords:

Machine Leaning, EfficientNet V2, Vision Transformer, Apple Leaf Diseases

Abstract

The apple farming industry faces challenges in managing apple leaf diseases. Current manual detection methods have limitations in expertise variability, time required, potential delays in identification leading to disease spread, and difficulty distinguishing diseases with similar visual symptoms. This research aims to develop an accurate, efficient, and automated apple leaf disease classification system using a hybrid approach that combines EfficientNet V2 architecture and Vision Transformer. The main objectives are to improve disease detection accuracy, reduce computational requirements, facilitate more effective plant management, and support modern agricultural practices in the apple industry. This research uses a hybrid deep learning model that integrates EfficientNet V2 and Vision Transformer components. Experiments were conducted on an apple leaf disease dataset to evaluate model performance. Results show the effectiveness of this method in classifying apple leaf diseases, achieving 98.56% accuracy and an F1 score of 0.9856 on test data. The proposed model has 15.6 million parameters, lighter than the original EfficientNetV2S model with 20 million parameters. Training time was reduced to 6 minutes 32 seconds compared to the original EfficientNetV2S model that required 8 minutes 41 seconds for 5 epochs on the same dataset.

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References

[1] BPS, “Produksi Tanaman Buah-buahan 2021-2023,” 2024. [Online]. Available: URL:https://www.bps.go.id/id/statistics-table/2/NjIjMg==/produksi-tanamanbuah-buahan.html.

[2] Yeniartha, “Upgrade Kapasitas Dan Kelembagaan Petani, Kementan Tingkatkan Produksi Komoditas Apel Malang,” 2024. [Online]. Available: URL:https://bbppketindan.bppsdmp.pertanian.go.id/blog/post/upgradekapasitas-dan-kelembagaan-petani-kementan-tingkatkan-produksi-komoditasapel-malang.

[3] Bansal, P., Kumar, R., & Kumar, S. (2021). Disease detection in apple leaves using deep convolutional neural network. Agriculture, 11(7), 617.

[4] Khan, A. I., Quadri, S. M. K., Banday, S., & Shah, J. L. (2022). Deep diagnosis: A real-time apple leaf disease detection system based on deep learning. computers and Electronics in Agriculture, 198, 107093.

[5] Gao, Y., Cao, Z., Cai, W., Gong, G., Zhou, G., & Li, L. (2023). Apple leaf disease identification in complex background based on BAM-net. Agronomy, 13(5), 1240.

[6] Vishnoi, V. K., Kumar, K., Kumar, B., Mohan, S., & Khan, A. A. (2022). Detection of apple plant diseases using leaf images through convolutional neural network. IEEE Access, 11, 6594-6609.

[7] Paymode, A. S., & Malode, V. B. (2022). Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG. Artificial Intelligence in Agriculture, 6, 23-33.

[8] Demilie, W. B. (2024). Plant disease detection and classification techniques: a comparative study of the performances. Journal of Big Data, 11(1), 5.

[9] M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” Machine Learning, vol. 97, pp. 6105–6114, September 2020.

[10] M. Tan and Q. V. Le, “EfficientNetV2: Smaller Models and Faster Training,” Computer Vision and Pattern Recognition, vol. 139, pp. 10096–10106, June 2021.

[11] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” Computer Vision and Pattern Recognition, vol. 26, pp. 6-8, June 2021.

[12] Hayat, M., Ahmad, N., Nasir, A., & Tariq, Z. A. (2024). Hybrid Deep Learning EfficientNetV2 and Vision Transformer (EffNetV2-ViT) Model for Breast Cancer Histopathological Image Classification. IEEE Access.

[13] Boudouh, N. (2025). Incorporating Deep Learning and Optimization Techniques with Data Augmentation for Improved Image Analysis and Classification (Doctoral dissertation, Université Mohamed Khider (Biskra-Algérie)).

[14] Cheung, W. K., Pakzad, A., Mogulkoc, N., Needleman, S. H., Rangelov, B., Gudmundsson, E., ... & Jacob, J. (2024). Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance. Journal of big Data, 11(1), 104.

[15] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, “Attention Is All You Need,” Computation and Language, vol. 30, August 2023.

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Published

19-05-2025

How to Cite

Mas Diyasa, I. G. S., Hadi, S., Nugroho, B., & Santoso, S. F. (2025). Implementation Of Hybrid EfficientNet V2 And Vision Transformer for Apple Leaf Diseases Classification. Information Technology International Journal, 3(1). https://doi.org/10.33005/itij.v3i1.42