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Adaptive preprocessing and Cascaded Canny Edge Segmentation for cassava disease identification using

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  • 1Department of Computer Science and Engineering, Excel Engineering College, Namakkal, Tamil Nadu, India.

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|January 5, 2026
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Summary

This study introduces an AI system for accurate cassava disease detection using HyperCapsInception-ResNet-V2-CNN and optimal feature selection. The system achieved high accuracy, improving precision farming and early disease identification.

Keywords:
affected regioncassava diseaseclassificationdeep learningenhanced contrastfeature optimizationsegmentation

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Cassava is a vital global crop, but identifying leaf diseases is challenging due to complex feature interdependencies.
  • Previous methods suffered from high false positives and misidentification, reducing accuracy in disease detection.

Purpose of the Study:

  • To develop an efficient AI-powered image analysis system for enhanced cassava disease detection.
  • To improve the accuracy and precision of identifying disease regions in cassava leaves.

Main Methods:

  • Utilized a dataset of 21,367 cassava leaf images from Kaggle.
  • Employed adaptive Gaussian Otsu thresholding, histogram color evaluation, and iterative clustering for data normalization and segmentation.
  • Applied Cascaded Canny Edge Segmentation (CCES) and Optimal Spider Swarm Intelligence Technique (OSSIT) for feature selection and dimension reduction.
  • Classified diseases using a HyperCapsInception-ResNet-V2-CNN model.

Main Results:

  • Achieved 98.15% accuracy, 97.22% F1-score, and 96.02% precision.
  • Outperformed traditional methods including EfficientNetB3, AlexNet, Faster-RCNN, and InceptionV3.
  • Demonstrated significant enhancement in detection and classification accuracy.

Conclusions:

  • The proposed AI system effectively automates cassava disease detection.
  • Optimized feature selection and the hybrid CNN architecture are key to improved accuracy.
  • The system shows high potential for practical application in precision agriculture and early disease management.