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Grape Leaf Disease Classification Combined with U-Net++ Network and Threshold Segmentation.

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This study segments grape leaves from complex backgrounds using U-net++ and identifies diseased areas with OTSU thresholding and EXG algorithms. Automated grading of grape leaf disease is achieved by analyzing the ratio of healthy green leaf area.

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Area of Science:

  • Agricultural technology
  • Computer vision
  • Plant pathology

Background:

  • Accurate segmentation of grape leaves from complex backgrounds is crucial for disease detection.
  • Existing methods struggle with intricate natural environments.
  • Automated disease analysis requires precise leaf and lesion identification.

Purpose of the Study:

  • To develop an automated system for segmenting grape leaves from complex backgrounds.
  • To accurately extract diseased and healthy regions on grape leaves.
  • To enable automatic grading of grape leaf disease severity.

Main Methods:

  • Utilized U-net++ convolutional neural network for semantic segmentation of grape leaves.
  • Employed OTSU threshold segmentation combined with the EXG algorithm to isolate diseased and healthy leaf areas.
  • Calculated the ratio of healthy green leaf area to total leaf area for disease grading.

Main Results:

  • Achieved effective segmentation of grape leaves amidst complex backgrounds.
  • Successfully extracted diseased spots and healthy regions using the developed algorithm.
  • Demonstrated automated grading of grape leaf disease based on quantitative area analysis.

Conclusions:

  • The U-net++ and OTSU+EXG approach provides an effective solution for automated grape leaf disease analysis.
  • This method enhances the accuracy and efficiency of disease identification in viticulture.
  • The automated grading system has potential for practical application in disease management.