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<p>Accurate recognition of potato leaf diseases plays an essential role in precision agriculture and crop protection. Traditional convolutional neural networks achieve promising results but mainly rely on texture and colour cues, neglecting lesion geometry and morphological evolution. This paper introduces a morphology-aware convolution (MAC) framework that embeds learnable morphological operations into deep feature extraction. The learnable morphological kernel adaptively performs differentiable dilation and erosion to capture lesion boundaries, while the structure-guided fusion module integrates structural and textural cues through adaptive weighting. Experiments on the PlantVillage-Potato dataset demonstrate that MAC achieves higher accuracy and robustness than existing convolutional and attention-based architectures with minimal parameter overhead. The method effectively enhances lesion boundary perception and shape consistency, offering improved interpretability and generalisation. These findings suggest that embedding morphological priors into neural networks provides a principled and efficient approach for fine-grained plant disease recognition and other structure-aware visual analysis tasks.</p>

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Morphology-Aware Convolution for Structure-Sensitive Potato Leaf Disease Recognition

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Wenyan Sun,
  • Xiaoyu Tang,
  • Feifei Xie

摘要

Accurate recognition of potato leaf diseases plays an essential role in precision agriculture and crop protection. Traditional convolutional neural networks achieve promising results but mainly rely on texture and colour cues, neglecting lesion geometry and morphological evolution. This paper introduces a morphology-aware convolution (MAC) framework that embeds learnable morphological operations into deep feature extraction. The learnable morphological kernel adaptively performs differentiable dilation and erosion to capture lesion boundaries, while the structure-guided fusion module integrates structural and textural cues through adaptive weighting. Experiments on the PlantVillage-Potato dataset demonstrate that MAC achieves higher accuracy and robustness than existing convolutional and attention-based architectures with minimal parameter overhead. The method effectively enhances lesion boundary perception and shape consistency, offering improved interpretability and generalisation. These findings suggest that embedding morphological priors into neural networks provides a principled and efficient approach for fine-grained plant disease recognition and other structure-aware visual analysis tasks.