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Volume 14
Issue 5
10.3390/computers14050197
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Novel MaxViT Model for Accelerated and Precise Soybean Leaf and Seed Disease Identification

by
Al Shahriar Uddin Khondakar Pranta
1,
Hasib Fardin
2,
Jesika Debnath
3,
Amira Hossain
3,
Anamul Haque Sakib
4,
Md. Redwan Ahmed
5,
Rezaul Haque
5,
Ahmed Wasif Reza
5,* and
M. Ali Akber Dewan
6,*
1
Department of Computer Science, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435, USA
2
Department of Engineering Management, Westcliff University, Irvine, CA 92614, USA
3
Department of Computer Science, Westcliff University, Irvine, CA 92614, USA
4
Department of Business Administration, International American University, 3440 Wilshire Blvd STE 1000, Los Angeles, CA 90010, USA
5
Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
6
School of Computing and Information Systems, Athabasca University, Athabasca, AB T9S 3A3, Canada
*
Authors to whom correspondence should be addressed.
Computers 2025, 14(5), 197; https://doi.org/10.3390/computers14050197 (registering DOI)
Submission received: 21 March 2025 / Revised: 14 May 2025 / Accepted: 15 May 2025 / Published: 18 May 2025
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision (2nd Edition))
Versions Notes

Abstract

Timely diagnosis of soybean diseases is essential to protect yields and limit global economic loss, yet current deep learning approaches suffer from small, imbalanced datasets, single‑organ focus, and limited interpretability. We propose MaxViT‑XSLD (MaxViT XAI-Seed–Leaf-Diagnostic), a Vision Transformer that integrates multiaxis attention with MBConv layers to jointly classify soybean leaf and seed diseases while remaining lightweight and explainable. Two benchmark datasets were upscaled through elastic deformation, Gaussian noise, brightness shifts, rotation, and flipping, enlarging ASDID from 10,722 to 16,000 images (eight classes) and the SD set from 5513 to 10,000 images (five classes). Under identical augmentation and hyperparameters, MaxViT‑XSLD delivered 99.82% accuracy on ASDID and 99.46% on SD, surpassing competitive ViT, CNN, and lightweight SOTA variants. High PR‑AUC and MCC values, confirmed via 10‑fold stratified cross‑validation and Wilcoxon tests, demonstrate robust generalization across data splits. Explainable AI (XAI) techniques further enhanced interpretability by highlighting biologically relevant features influencing predictions. Its modular design also enables future model compression for edge deployment in resource‑constrained settings. Finally, we deploy the model in SoyScan, a real‑time web tool that streams predictions and visual explanations to growers and agronomists. These findings establishes a scalable, interpretable system for precision crop health monitoring and lay the groundwork for edge‑oriented, multimodal agricultural diagnostics.
Keywords: soybean disease; agriculture diagnosis; vision transformer; computer vision; plant disease; diagnostic tool soybean disease; agriculture diagnosis; vision transformer; computer vision; plant disease; diagnostic tool

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MDPI and ACS Style

Pranta, A.S.U.K.; Fardin, H.; Debnath, J.; Hossain, A.; Sakib, A.H.; Ahmed, M.R.; Haque, R.; Reza, A.W.; Dewan, M.A.A. A Novel MaxViT Model for Accelerated and Precise Soybean Leaf and Seed Disease Identification. Computers 2025, 14, 197. https://doi.org/10.3390/computers14050197

AMA Style

Pranta ASUK, Fardin H, Debnath J, Hossain A, Sakib AH, Ahmed MR, Haque R, Reza AW, Dewan MAA. A Novel MaxViT Model for Accelerated and Precise Soybean Leaf and Seed Disease Identification. Computers. 2025; 14(5):197. https://doi.org/10.3390/computers14050197

Chicago/Turabian Style

Pranta, Al Shahriar Uddin Khondakar, Hasib Fardin, Jesika Debnath, Amira Hossain, Anamul Haque Sakib, Md. Redwan Ahmed, Rezaul Haque, Ahmed Wasif Reza, and M. Ali Akber Dewan. 2025. "A Novel MaxViT Model for Accelerated and Precise Soybean Leaf and Seed Disease Identification" Computers 14, no. 5: 197. https://doi.org/10.3390/computers14050197

APA Style

Pranta, A. S. U. K., Fardin, H., Debnath, J., Hossain, A., Sakib, A. H., Ahmed, M. R., Haque, R., Reza, A. W., & Dewan, M. A. A. (2025). A Novel MaxViT Model for Accelerated and Precise Soybean Leaf and Seed Disease Identification. Computers, 14(5), 197. https://doi.org/10.3390/computers14050197

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