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Crop diseases caused by a variety of fungi, viruses, bacteria and insects has been a serious problem that requires global attention for generations. Therefore, as soon as crop disease diagnosis should be done to prevent yield loss and increase monetary value. In this paper, the Autonomous Attention Feedback Network (AAFN) model is proposed to produce Super Resolution images. The proposed AAFN model obtained PSNR 30.35, 36.469, 37.846 and SSIM 0.7262, 0.9934, 0.9395 for super-resolution factors 2, 4 and 6 respectively, with classification accuracies of 99.73, 98.62 and 97.18.

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Autonomous Attention Feedback Network (AAFN) Based Super-Resolution for Potato Leaf Disease Detection

  • 会议论文

  • 发表日期：

    2025年

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  • P. V. Yeswanth,
  • Ananya Khera,
  • S. Deivalakshmi

摘要

Crop diseases caused by a variety of fungi, viruses, bacteria and insects has been a serious problem that requires global attention for generations. Therefore, as soon as crop disease diagnosis should be done to prevent yield loss and increase monetary value. In this paper, the Autonomous Attention Feedback Network (AAFN) model is proposed to produce Super Resolution images. The proposed AAFN model obtained PSNR 30.35, 36.469, 37.846 and SSIM 0.7262, 0.9934, 0.9395 for super-resolution factors 2, 4 and 6 respectively, with classification accuracies of 99.73, 98.62 and 97.18.