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Lightweight Plant Leaf Disease Classification Using CLAHE-Enhanced EfficientNet-B0 with CBAM Attention

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2026, 12(5), 2
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : August 6, 2026
  • Accepted : October 12, 2026
  • Published : October 31, 2026

Asif Muhammad Sheharyar 1,  KIM, SOO KYUN ORD ID 2

1제주대학교 컴퓨터공학과
2제주대학교

Accredited

ABSTRACT

Plant leaf diseases threaten crop yield and quality, while manual diagnosis requires considerable time and expert knowledge. This paper proposes a lightweight plant leaf disease classification framework combining Contrast Limited Adaptive Histogram Equalization (CLAHE), EfficientNet-B0, and the Convolutional Block Attention Module (CBAM). CLAHE enhances lesion contrast, EfficientNet-B0 extracts hierarchical features, and CBAM emphasizes disease-relevant features. The proposed framework was evaluated using 20,638 images from 15 PlantVillage classes through repeated experiments with three random seeds (42, 52, and 62) and compared with MobileNetV2, ShuffleNetV2, and SqueezeNet1.1. Experimental results showed that the proposed model achieved the highest classification performance, with a mean accuracy of 99.860 ± 0.019% and a macro-F1 score of 99.868 ± 0.036%. These findings suggest that the proposed framework has potential as a lightweight model for plant leaf disease classification in resource-constrained computing environments.

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