Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model

Erdal Özbay; Feyza Altunbey Özbay; Farhad Soleımanıan Gharehchopogh

doi:10.15832/ankutbd.1857033

Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model

Abstract

The identification of plant diseases plays a crucial role in sustaining agricultural productivity and minimizing economic losses. Traditional approaches, which often depend on visual assessment and the farmer’s experience, are typically inadequate for the timely recognition of infections, allowing diseases to progress and cause substantial damage. In overcoming these challenges, deep learning methods offer greater capability in solving complex classification problems than traditional machine learning algorithms. In this study, we propose a hybrid transformer-driven framework for high-precision disease detection on mango leaves. This approach combines mango leaf vein segmentation with transformer-based feature extraction. MaxViT and Swin models derive 512 and 768 features from each image, which are then combined to form a 1280-dimensional feature vector. The feature attention mechanism highlights the most informative components of the features, while the improved grey wolf optimizer reduces the increased dimensionality. 200 discriminative features were selected from the feature vector, and the decreasing features were classified using six machine learning classifiers. Experiments were performed on the MangoLeafBD dataset, which contains eight classes: seven diseases and a healthy class. The proposed MaxViT-Swin–IGWO hybrid framework achieved remarkable results, achieving 100% accuracy for the Linear Discriminant classifier and 99.98% accuracy for the Neural Network classifier. Performance analysis was accomplished using precision, recall, F1-score, dice, and ROC criteria. Furthermore, an ablation test was conducted to evaluate the impact of individual model variations on the preprocessing pipeline. The findings revealed that the proposed MaxViT Swin–IGWO hybrid framework detects mango leaf diseases with superior performance, outperforming both conventional and contemporary alternatives.

Keywords

References

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Details

Primary Language

English

Subjects

Medicinal and Aromatic Plants

Journal Section

Research Article

Authors

Erdal Özbay ^*
0000-0002-9004-4802
Türkiye

Feyza Altunbey Özbay
0000-0003-0629-6888
Türkiye

Farhad Soleımanıan Gharehchopogh
0000-0003-1588-1659
Iran

Publication Date

July 28, 2026

Submission Date

January 5, 2026

Acceptance Date

February 24, 2026

Published in Issue

Year 2026 Volume: 32 Number: 3

DOI

https://doi.org/10.15832/ankutbd.1857033

IZ

https://izlik.org/JA95CA67SB

Cite

RIS / Bibtex

APA

Özbay, E., Altunbey Özbay, F., & Soleımanıan Gharehchopogh, F. (2026). Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model. Journal of Agricultural Sciences, 32(3), 682-700. https://doi.org/10.15832/ankutbd.1857033

AMA

1.Özbay E, Altunbey Özbay F, Soleımanıan Gharehchopogh F. Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model. J Agr Sci-Tarim Bili. 2026;32(3):682-700. doi:10.15832/ankutbd.1857033

Chicago

Özbay, Erdal, Feyza Altunbey Özbay, and Farhad Soleımanıan Gharehchopogh. 2026. “Automated Mango Leaf Disease Identification With Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model”. Journal of Agricultural Sciences 32 (3): 682-700. https://doi.org/10.15832/ankutbd.1857033.

EndNote

Özbay E, Altunbey Özbay F, Soleımanıan Gharehchopogh F (July 1, 2026) Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model. Journal of Agricultural Sciences 32 3 682–700.

IEEE

[1]E. Özbay, F. Altunbey Özbay, and F. Soleımanıan Gharehchopogh, “Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model”, J Agr Sci-Tarim Bili, vol. 32, no. 3, pp. 682–700, July 2026, doi: 10.15832/ankutbd.1857033.

ISNAD

Özbay, Erdal - Altunbey Özbay, Feyza - Soleımanıan Gharehchopogh, Farhad. “Automated Mango Leaf Disease Identification With Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model”. Journal of Agricultural Sciences 32/3 (July 1, 2026): 682-700. https://doi.org/10.15832/ankutbd.1857033.

JAMA

1.Özbay E, Altunbey Özbay F, Soleımanıan Gharehchopogh F. Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model. J Agr Sci-Tarim Bili. 2026;32:682–700.

MLA

Özbay, Erdal, et al. “Automated Mango Leaf Disease Identification With Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model”. Journal of Agricultural Sciences, vol. 32, no. 3, July 2026, pp. 682-00, doi:10.15832/ankutbd.1857033.

Vancouver

1.Erdal Özbay, Feyza Altunbey Özbay, Farhad Soleımanıan Gharehchopogh. Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model. J Agr Sci-Tarim Bili. 2026 Jul. 1;32(3):682-700. doi:10.15832/ankutbd.1857033