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

Adnan Gökten; Erkut Tekeli; Hasan Beytullah Dönmez

doi:10.15832/ankutbd.1819492

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

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.

Keywords

References

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Details

Primary Language

English

Subjects

Modelling and Simulation, Artificial Intelligence (Other), Plant Protection (Other)

Journal Section

Research Article

Authors

Adnan Gökten ^*
0000-0001-8988-9720
Türkiye

Erkut Tekeli
0000-0001-9468-5378
Türkiye

Hasan Beytullah Dönmez
0000-0003-1495-4553
Türkiye

Publication Date

July 28, 2026

Submission Date

November 7, 2025

Acceptance Date

February 28, 2026

Published in Issue

Year 2026 Volume: 32 Number: 3

DOI

https://doi.org/10.15832/ankutbd.1819492

IZ

https://izlik.org/JA49CK23RF

Cite

RIS / Bibtex

APA

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, 32(3), 701-717. https://doi.org/10.15832/ankutbd.1819492

AMA

1.Gökten A, Tekeli E, Dönmez HB. Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM. J Agr Sci-Tarim Bili. 2026;32(3):701-717. doi:10.15832/ankutbd.1819492

Chicago

Gökten, Adnan, Erkut Tekeli, and Hasan Beytullah Dönmez. 2026. “Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM”. Journal of Agricultural Sciences 32 (3): 701-17. https://doi.org/10.15832/ankutbd.1819492.

EndNote

Gökten A, Tekeli E, Dönmez HB (July 1, 2026) Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM. Journal of Agricultural Sciences 32 3 701–717.

IEEE

[1]A. Gökten, E. Tekeli, and H. B. Dönmez, “Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM”, J Agr Sci-Tarim Bili, vol. 32, no. 3, pp. 701–717, July 2026, doi: 10.15832/ankutbd.1819492.

ISNAD

Gökten, Adnan - Tekeli, Erkut - Dönmez, Hasan Beytullah. “Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM”. Journal of Agricultural Sciences 32/3 (July 1, 2026): 701-717. https://doi.org/10.15832/ankutbd.1819492.

JAMA

1.Gökten A, Tekeli E, Dönmez HB. Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM. J Agr Sci-Tarim Bili. 2026;32:701–717.

MLA

Gökten, Adnan, et al. “Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM”. Journal of Agricultural Sciences, vol. 32, no. 3, July 2026, pp. 701-17, doi:10.15832/ankutbd.1819492.

Vancouver

1.Adnan Gökten, Erkut Tekeli, Hasan Beytullah Dönmez. Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM. J Agr Sci-Tarim Bili. 2026 Jul. 1;32(3):701-17. doi:10.15832/ankutbd.1819492