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This research enhances Fusarium Head Blight (FHB) detection in hyperspectral images using a Deep Convolutional Neural Network (DCNN) with Principal Component Analysis (PCA) and a Spectral Attention Module (SAM). By reducing spectral dimensionality with PCA and applying channel attention, the model improves feature representation. Tested on the AI for Agriculture 2024 dataset, it achieved 97.06% accuracy, outperforming the baseline’s 81.76%. Ablation studies confirmed PCA’s key role, while SAM had limited impact. These results highlight the benefits of spectral feature selection for more precise and scalable agricultural disease monitoring.

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Channel Attention for Fusarium Head Blight Detection in Hyperspectral Images

  • 会议论文

  • 发表日期：

    2026年

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  • Lily Akpanke,
  • Dustin van der Haar,
  • Hima Vadapalli

摘要

This research enhances Fusarium Head Blight (FHB) detection in hyperspectral images using a Deep Convolutional Neural Network (DCNN) with Principal Component Analysis (PCA) and a Spectral Attention Module (SAM). By reducing spectral dimensionality with PCA and applying channel attention, the model improves feature representation. Tested on the AI for Agriculture 2024 dataset, it achieved 97.06% accuracy, outperforming the baseline’s 81.76%. Ablation studies confirmed PCA’s key role, while SAM had limited impact. These results highlight the benefits of spectral feature selection for more precise and scalable agricultural disease monitoring.