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Grapevine disease detection using (q,τ)-nabla calculus quantum deformation with deep learning features.

Ahmad Sami Al-Shamayleh1, Rabha W Ibrahim2

  • 1Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Al-Salt, Amman 19328, Jordan.

Methodsx
|September 29, 2025
PubMed
Summary
This summary is machine-generated.

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Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...
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This study introduces a hybrid method for detecting grapevine leaf diseases using quantum deformation features and deep learning. The novel approach enhances disease classification accuracy, aiding sustainable agriculture and food security.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate plant disease detection is crucial for sustainable agriculture and food security.
  • Manual diagnosis of plant diseases is labor-intensive and requires specialized knowledge.
  • Advancements in computer vision and imaging technologies enable quantitative analysis of plant physiology.

Purpose of the Study:

  • To propose a novel hybrid classification method for identifying and classifying grapevine leaf diseases.
  • To combine (q,τ)-Nabla calculus quantum deformation features with deep learning features for improved disease detection.
  • To enhance the accuracy and efficiency of plant disease management.

Main Methods:

  • Extraction of robust handcrafted features using (q,τ)-Nabla calculus quantum deformation to capture local texture and structural variations.
Keywords:
(q,τ)-Nabla calculus quantum deformationClassificationFeature extractionGrapevine disease detection

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  • Extraction of high-level semantic features using a pre-trained convolutional neural network (CNN) on leaf images.
  • Concatenation of handcrafted and deep features followed by classification using a machine learning model.

    • Main Results:

    • The proposed hybrid method demonstrated superior accuracy in classifying grapevine leaf diseases compared to individual feature extraction approaches.
    • The integration of quantum deformation features and deep learning features significantly improved classification performance.
    • The method effectively identified disease symptoms by analyzing both texture and high-level image semantics.

    Conclusions:

    • The developed hybrid classification method offers a promising solution for accurate and efficient plant disease detection.
    • This approach supports effective plant disease management, reduces financial losses, and contributes to food security.
    • The study highlights the potential of combining novel mathematical calculus with deep learning for agricultural applications.