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<p>Accurate fruit disease classification is critical for agricultural productivity, yet traditional manual methods remain labour-intensive and error-prone. This study proposes a&#xa0;hybrid model that synergistically integrates multi-architecture convolutional neural network models such as ResNet, DenseNet, and EfficientNet to address challenges such as intra-class variability and dataset imbalances. The model has Mobile Inverted Bottleneck Convolution with Squeeze-and-Excitation blocks that help it prioritize useful features. It also has residual pathways for gradient optimization and data augmentation that makes it easier to generalize. The results are evaluated on 9714 apple disease images (four classes), 3403 guava disease images (three classes), and 5000 mango disease images (five classes), the hybrid model achieves perfect performance, with 99.99% accuracy, precision, recall, and F1-score for apple diseases, 99.60% accuracy for guava diseases, and 99.68% accuracy for mango diseases, surpassing state-of-the-art methods. Key innovations include dense connectivity for feature reuse, channel-wise attention mechanisms, and computational efficiency through global average pooling. The results underscore the model’s robustness and scalability, offering a&#xa0;practical solution for real-time disease detection in smart farming systems.</p>

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Multi-architecture CNN Framework with MBConv-SE Optimization for Fruit Disease Classification

  • 期刊论文

  • 发表日期：

    2025年7月27日

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  • Anil Sandhi,
  • Rajeev Kumar,
  • Dinesh Kumar,
  • Reeta Bhardwaj

摘要

Accurate fruit disease classification is critical for agricultural productivity, yet traditional manual methods remain labour-intensive and error-prone. This study proposes a hybrid model that synergistically integrates multi-architecture convolutional neural network models such as ResNet, DenseNet, and EfficientNet to address challenges such as intra-class variability and dataset imbalances. The model has Mobile Inverted Bottleneck Convolution with Squeeze-and-Excitation blocks that help it prioritize useful features. It also has residual pathways for gradient optimization and data augmentation that makes it easier to generalize. The results are evaluated on 9714 apple disease images (four classes), 3403 guava disease images (three classes), and 5000 mango disease images (five classes), the hybrid model achieves perfect performance, with 99.99% accuracy, precision, recall, and F1-score for apple diseases, 99.60% accuracy for guava diseases, and 99.68% accuracy for mango diseases, surpassing state-of-the-art methods. Key innovations include dense connectivity for feature reuse, channel-wise attention mechanisms, and computational efficiency through global average pooling. The results underscore the model’s robustness and scalability, offering a practical solution for real-time disease detection in smart farming systems.