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<p>Accurate and rapid identification of potato diseases is essential for preserving yield and quality. However, existing deep learning models suffer from low accuracy and redundancy issues. Moreover, models trained on datasets with idealized backgrounds often fail to meet the requirements for potato disease identification under real-field conditions. To address the aforementioned challenges, this study proposes a potato disease identification model named Potato-ShuffleNetV2, based on an improved ShuffleNetV2 architecture. First, a grouped efficient channel attention (GECA) mechanism is introduced to suppress interference from natural field backgrounds. Subsequently, a feature fusion module is constructed to reduce the loss of critical information. The ReLU activation function is then replaced with Gaussian error linear unit (GELU) to enhance model robustness. Finally, model pruning is applied to remove redundant feature layers and reduce model complexity. The final model achieves an accuracy of 97.78% on the potato disease test dataset, with only 0.13&#xa0;M parameters and 0.03G FLOPs. Compared with state-of-the-art models such as ResNet50, DenseNet121, and EfficientNetV2_m, the proposed model improves classification accuracy by 1.53%, 2.29%, and 5.41%, respectively, while reducing the number of parameters by 99.45%, 98.13%, and 99.75%, respectively. Experimental results demonstrate that the proposed model achieves significant advantages in both recognition accuracy and model compactness, making it highly suitable for deployment in real-world field environments.</p>

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Potato-ShuffleNetV2: A Field Potato Disease Identification Model Based on the Improved ShuffleNetV2

  • 期刊论文

  • 发表日期：

    2025年9月6日

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  • Zuohua Wu,
  • Lin Su

摘要

Accurate and rapid identification of potato diseases is essential for preserving yield and quality. However, existing deep learning models suffer from low accuracy and redundancy issues. Moreover, models trained on datasets with idealized backgrounds often fail to meet the requirements for potato disease identification under real-field conditions. To address the aforementioned challenges, this study proposes a potato disease identification model named Potato-ShuffleNetV2, based on an improved ShuffleNetV2 architecture. First, a grouped efficient channel attention (GECA) mechanism is introduced to suppress interference from natural field backgrounds. Subsequently, a feature fusion module is constructed to reduce the loss of critical information. The ReLU activation function is then replaced with Gaussian error linear unit (GELU) to enhance model robustness. Finally, model pruning is applied to remove redundant feature layers and reduce model complexity. The final model achieves an accuracy of 97.78% on the potato disease test dataset, with only 0.13 M parameters and 0.03G FLOPs. Compared with state-of-the-art models such as ResNet50, DenseNet121, and EfficientNetV2_m, the proposed model improves classification accuracy by 1.53%, 2.29%, and 5.41%, respectively, while reducing the number of parameters by 99.45%, 98.13%, and 99.75%, respectively. Experimental results demonstrate that the proposed model achieves significant advantages in both recognition accuracy and model compactness, making it highly suitable for deployment in real-world field environments.