ARCHIVES
Original Article
Hybrid EfficientNet-B0 and Vision Transformer Framework for Context-Aware Crop Disease Detection from Agricultural Images
T C Swetha Priya1
Poosa Rushika2
Naragudam Sanjana3
Megi Sindhuja4
1 Assistant Professor, Department of Information Technology, Stanley College of Engineering & Technology for women, Hyderabad, Telangana, India. 2 3 4 UG Scholar, Department of Information Technology, Stanley College of Engineering & Technology for women, Hyderabad, Telangana, India.
Published Online: January-April 2026
Pages: 243-250
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501034
References
1) Shandilya, G., S.Gupta, H. G.Mohamed, S.Bharany, A. U.Rehman, and S.Hussen. 2025. “Enhanced Maize Leaf Disease Detection and
Classification Using an Integrated CNN-ViT Model.” Food Science & Nutrition13, no.7:e70513. https://doi.org/10.1002/fsn3.70513.
2) Sinamenye, J.H., Chatterjee, A. & Shrestha, R. Potato plant disease detection: leveraging hybrid deep learning models. BMC Plant Biol 25,
647 (2025).https://doi.org/10.1186/s12870-025-06679-4
3) Aboelenin, S., Elbasheer, F.A., Eltoukhy, M.M. et al. A hybrid Framework for plant leaf disease detection and classification using
convolutional neural networks and vision transformer. Complex Intell. Syst. 11, 142 (2025). https://doi.org/10.1007/s40747- 024-01764-x
4) Roy, P.S., Kukreja, V. Vision transformers for rice leaf disease detection and severity estimation: a precision agriculture approach. J. Saudi
Soc. Agric.Sci. 24,3(2025).https://doi.org/10.1007/s44447- 025-00007-w
5) Maeda-Gutiérrez, V., Oropeza-Valdez, J. J., Reveles-Gómez, L. C., Padron-Manrique, C., Resendis-Antonio, O., Solís-Sánchez, L. O.,
Guerrero-Osuna, H. A., & Olvera Olvera, C. A. (2025). AI-Driven Plant Health Assessment: A Comparative Analysis of Inception V3,
ResNet-50 and ViT with SHAP for Accurate Disease Identification in Taro.Agronomy,15(1),77.https://doi.org/10.3390/agro nomy15010077
6) Mehnaz, S., & Islam, M. T. (2025). Rice leaf disease detection: A comparative study between CNN, transformer and non-neural network
architectures (arXiv:2501.06740).
7) Salman Z, Muhammad A and Han D (2025) Plant disease classification in the wild using vision transformers and mixture of experts. Front.
Plant Sci. 16:1522985. doi: 10.3389/fpls.2025.1522985
8) Hemalatha, S., Jayachandran, J.J.B. A Multitask Learning-Based Vision Transformer for Plant Disease Localization and Classification. Int
J Comput Intell Syst 17, 188 (2024). https://doi.org/10.1007/s44196-024-00597-3
9) Borhani, Y., Khoramdel, J. & Najafi, E. A deep learning based approach for automated plant disease classification using vision
transformer. Sci Rep 12,11554(2022).https://doi.org/10.1038/s41598- 022-15163-0
10) Sun H, Fu R, Wang X, Wu Y, Al-Absi MA, Cheng Z, Chen Q, Sun Y. Efficient deep learning-based tomato leaf disease detection through
global and local feature fusion. BMC Plant Biol. 2025 Mar 11;25(1):311. doi: 10.1186/s12870-025-06247-w. PMID: 40069604; PMCID:
PMC11895386.
Classification Using an Integrated CNN-ViT Model.” Food Science & Nutrition13, no.7:e70513. https://doi.org/10.1002/fsn3.70513.
2) Sinamenye, J.H., Chatterjee, A. & Shrestha, R. Potato plant disease detection: leveraging hybrid deep learning models. BMC Plant Biol 25,
647 (2025).https://doi.org/10.1186/s12870-025-06679-4
3) Aboelenin, S., Elbasheer, F.A., Eltoukhy, M.M. et al. A hybrid Framework for plant leaf disease detection and classification using
convolutional neural networks and vision transformer. Complex Intell. Syst. 11, 142 (2025). https://doi.org/10.1007/s40747- 024-01764-x
4) Roy, P.S., Kukreja, V. Vision transformers for rice leaf disease detection and severity estimation: a precision agriculture approach. J. Saudi
Soc. Agric.Sci. 24,3(2025).https://doi.org/10.1007/s44447- 025-00007-w
5) Maeda-Gutiérrez, V., Oropeza-Valdez, J. J., Reveles-Gómez, L. C., Padron-Manrique, C., Resendis-Antonio, O., Solís-Sánchez, L. O.,
Guerrero-Osuna, H. A., & Olvera Olvera, C. A. (2025). AI-Driven Plant Health Assessment: A Comparative Analysis of Inception V3,
ResNet-50 and ViT with SHAP for Accurate Disease Identification in Taro.Agronomy,15(1),77.https://doi.org/10.3390/agro nomy15010077
6) Mehnaz, S., & Islam, M. T. (2025). Rice leaf disease detection: A comparative study between CNN, transformer and non-neural network
architectures (arXiv:2501.06740).
7) Salman Z, Muhammad A and Han D (2025) Plant disease classification in the wild using vision transformers and mixture of experts. Front.
Plant Sci. 16:1522985. doi: 10.3389/fpls.2025.1522985
8) Hemalatha, S., Jayachandran, J.J.B. A Multitask Learning-Based Vision Transformer for Plant Disease Localization and Classification. Int
J Comput Intell Syst 17, 188 (2024). https://doi.org/10.1007/s44196-024-00597-3
9) Borhani, Y., Khoramdel, J. & Najafi, E. A deep learning based approach for automated plant disease classification using vision
transformer. Sci Rep 12,11554(2022).https://doi.org/10.1038/s41598- 022-15163-0
10) Sun H, Fu R, Wang X, Wu Y, Al-Absi MA, Cheng Z, Chen Q, Sun Y. Efficient deep learning-based tomato leaf disease detection through
global and local feature fusion. BMC Plant Biol. 2025 Mar 11;25(1):311. doi: 10.1186/s12870-025-06247-w. PMID: 40069604; PMCID:
PMC11895386.
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