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Journals / Tarım Bilimleri Dergisi / 2026 / Volume: 32 Issue: 4

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

Kürşad Buğrahan Yapar(KONYA TEKNİK ÜNİVERSİTESİ)0009-0006-7666-6177
Nurettin Doğan(SELÇUK ÜNİVERSİTESİ)0000-0002-8267-8469
Özgür Öksüz(KONYA TEKNİK ÜNİVERSİTESİ)0000-0001-5568-6116
Pages
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Publication Date
2026-10-12
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Orange diseases significantly threaten fruit quality and agricultural productivity, resulting in substantial economic losses.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:ConvNeXt · EfficientNet · Orange Fruit

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.

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Cite This Article

Yapar, K. B., Doğan, N., Öksüz, Ö. (2026). Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection. Tarım Bilimleri Dergisi, Volume: 32 Issue: 4. https://doi.org/10.15832/ankutbd.1850038

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