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

Abstract

Today, one of the most important first steps in attaining sustainable agriculture and guaranteeing food security is the detection of plant diseases. Quantitative analysis of plant physiology is now feasible thanks to developments in computer vision and imaging technologies. On the other hand, manual diagnosis requires a lot of work and in-depth plant pathology knowledge. Numerous innovative methods for identifying and classifying plant diseases have been widely used. In this study, we propose a novel hybrid classification method that combines (q,τ)-Nabla calculus quantum deformation-based features with deep learning feature representations to classify diseases in grapevine leaves. The methodology of this study relies on:

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  Nabla calculus quantum deformation features are utilized to extract robust handcrafted features that capture local texture and structural variations associated with disease symptoms.
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  Deep features are extracted using a pre-trained convolutional neural network, which captures high-level semantic information from leaf images.

The concatenated feature vectors are then fed into a machine learning classifier for final prediction. Test results on a dataset of grapevine leaf disease show that the proposed method outperforms individual approaches, in accuracy. The proposed method helps minimize financial losses and support effective plant disease management, thereby improving crop yield and contributing to food security.