Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection

Kürşad Buğrahan Yapar; Nurettin Doğan; Özgür Öksüz

doi:10.15832/ankutbd.1850038

Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection

Abstract

Orange diseases significantly threaten fruit quality and agricultural productivity, resulting in substantial economic losses. Traditional diagnostic methods are limited by their labor-intensive nature, high costs, and reliance on subjective expert evaluation, increasing the demand for automated solutions. This study systematically compared three state-of the-art deep learning architectures-EfficientNet-B0, ConvNeXt-Tiny, and Vision Transformer (ViT-B/16)-on the Orange Fruit dataset, which comprises healthy, citrus canker, and melanose samples. To enhance generalization and mitigate overfitting, transfer learning with ImageNet pretraining was combined with advanced data augmentation strategies like Mixup and Cutout. Experimental results demonstrated that the ConvNeXt-Tiny architecture, supported by these techniques, achieved superior performance, reaching 98.76% Accuracy, 99% F1-Score, and 99.95% ROC-AUC. Beyond numerical metrics, model transparency was investigated using Explainable Artificial Intelligence (XAI). The study evaluated the widely used Gradient-weighted Class Activation Mapping (Grad-CAM) method alongside Randomized Input Sampling for Explanation (RISE), which, to the best of our knowledge, represents one of the first applications of RISE in plant disease image analysis. RISE provided more comprehensive visualizations of the fruit surface regions influencing decisions, thereby strengthening interpretability. These findings confirm the high potential of modern deep learning for early disease diagnosis and highlight that XAI integration significantly enhances the reliability of agricultural decision support systems. Five fold cross-validation experiments and paired t-test analysis further confirmed the robustness and statistical significance of these findings.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Agricultural Automatization, Agricultural Biotechnology Diagnostics

Journal Section

Research Article

Authors

Kürşad Buğrahan Yapar ^*
0009-0006-7666-6177
Türkiye

Nurettin Doğan
0000-0002-8267-8469
Türkiye

Özgür Öksüz
0000-0001-5568-6116
Türkiye

Publication Date

October 12, 2026

Submission Date

December 26, 2025

Acceptance Date

May 21, 2026

Published in Issue

Year 2026 Volume: 32 Number: 4

DOI

https://doi.org/10.15832/ankutbd.1850038

IZ

https://izlik.org/JA66PF64AM

Cite

RIS / Bibtex

APA

Yapar, K. B., Doğan, N., & Öksüz, Ö. (2026). Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection. Journal of Agricultural Sciences, 32(4), 1062-1084. https://doi.org/10.15832/ankutbd.1850038

AMA

1.Yapar KB, Doğan N, Öksüz Ö. Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection. J Agr Sci-Tarim Bili. 2026;32(4):1062-1084. doi:10.15832/ankutbd.1850038

Chicago

Yapar, Kürşad Buğrahan, Nurettin Doğan, and Özgür Öksüz. 2026. “Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection”. Journal of Agricultural Sciences 32 (4): 1062-84. https://doi.org/10.15832/ankutbd.1850038.

EndNote

Yapar KB, Doğan N, Öksüz Ö (October 1, 2026) Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection. Journal of Agricultural Sciences 32 4 1062–1084.

IEEE

[1]K. B. Yapar, N. Doğan, and Ö. Öksüz, “Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection”, J Agr Sci-Tarim Bili, vol. 32, no. 4, pp. 1062–1084, Oct. 2026, doi: 10.15832/ankutbd.1850038.

ISNAD

Yapar, Kürşad Buğrahan - Doğan, Nurettin - Öksüz, Özgür. “Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection”. Journal of Agricultural Sciences 32/4 (October 1, 2026): 1062-1084. https://doi.org/10.15832/ankutbd.1850038.

JAMA

1.Yapar KB, Doğan N, Öksüz Ö. Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection. J Agr Sci-Tarim Bili. 2026;32:1062–1084.

MLA

Yapar, Kürşad Buğrahan, et al. “Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection”. Journal of Agricultural Sciences, vol. 32, no. 4, Oct. 2026, pp. 1062-84, doi:10.15832/ankutbd.1850038.

Vancouver

1.Kürşad Buğrahan Yapar, Nurettin Doğan, Özgür Öksüz. Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection. J Agr Sci-Tarim Bili. 2026 Oct. 1;32(4):1062-84. doi:10.15832/ankutbd.1850038