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Journals / Journal of Agricultural Sciences / 2026 / Volume: 32 Issue: 3

Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM

Adnan Gökten(Çukurova Üniversitesi)0000-0001-8988-9720
Erkut Tekeli(ADANA ALPARSLAN TURKES SCIENCE AND TECHNOLOGY UNIVERSITY)0000-0001-9468-5378
Hasan Beytullah Dönmez(CUKUROVA UNIVERSITY)0000-0003-1495-4553
Pages
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Publication Date
2026-07-28
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AI Summary & Key Takeaways

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

The experimental results demonstrate that ConvNeXt-Tiny achieves 99-100% accuracy across all plant species, MobileNetV2 attains 97-100% accuracy with lower computational requirements, and VGG16 yields 97-99.5% accuracy.

📌 3 Key Takeaways

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Objective & Hypothesis:Plant disease detection is critical for sustainable agriculture and food security.
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Key Findings & Data:This study presents a comparative evaluation of three convolutional neural network architectures (ConvNeXt-Tiny, MobileNetV2, and VGG16) for classifying potato, maize, and pepper leaf diseases, with emphasis on explainability through Gradient-weighted Class Activation Mapping (Grad-CAM).
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Conclusion & Impact:The experimental results demonstrate that ConvNeXt-Tiny achieves 99-100% accuracy across all plant species, MobileNetV2 attains 97-100% accuracy with lower computational requirements, and VGG16 yields 97-99.5% accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Explainable artificial intelligence · Grad-CAM · Plant disease classification

Abstract

Plant disease detection is critical for sustainable agriculture and food security. While deep learning models achieve high accuracy in leaf disease classification, their black box nature poses limitations for trust and adoption among agricultural practitioners. This study presents a comparative evaluation of three convolutional neural network architectures (ConvNeXt-Tiny, MobileNetV2, and VGG16) for classifying potato, maize, and pepper leaf diseases, with emphasis on explainability through Gradient-weighted Class Activation Mapping (Grad-CAM). The experimental results demonstrate that ConvNeXt-Tiny achieves 99-100% accuracy across all plant species, MobileNetV2 attains 97-100% accuracy with lower computational requirements, and VGG16 yields 97-99.5% accuracy. Grad-CAM visualizations reveal that modern architectures precisely focus on lesion regions, whereas older models occasionally attend to irrelevant features such as leaf veins and edges. Misclassification analysis identifies shadows and natural leaf patterns as primary error sources. This research demonstrates that explainable artificial intelligence is not merely complementary but essential for developing trustworthy agricultural decision support systems.

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Cite This Article

Gökten, A., Tekeli, E., Dönmez, H. B. (2026). Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM. Journal of Agricultural Sciences, Volume: 32 Issue: 3. https://doi.org/10.15832/ankutbd.1819492

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