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Adaptive Augmented Deep Learning with EfficientNetB3 for Robust Plant Disease Classification

  • Unique Paper ID: 177341
  • Volume: 11
  • Issue: 12
  • PageNo: 1798-1806
  • Abstract:
  • Plant diseases significantly threaten global agricultural productivity, leading to substantial economic losses and food insecurity. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have enabled automated and precise plant disease classification, reducing reliance on manual diagnosis. This paper proposes a novel approach integrating EfficientNetB3 with Adaptive Augmented Deep Learning (AADL) to enhance classification accuracy across multiple plant disease categories. The proposed model optimizes data augmentation strategies based on real-time performance feedback, ensuring better feature extraction and improved model generalization. Additionally, it leverages transfer learning to maximize efficiency, enabling faster convergence and reduced computational costs. Extensive experiments on benchmark datasets demonstrate superior performance compared to existing deep learning models, achieving state-of-the-art classification accuracy with improved robustness against variations in lighting, angle, and background noise
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 1798-1806

Adaptive Augmented Deep Learning with EfficientNetB3 for Robust Plant Disease Classification

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