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Related Experiment Videos

PD-ViCo: an explainable AI-based contrastive captioner vision transformer with patch dropout for multi-class brinjal

Abu Kowshir Bitto1, Md Hasan Imam Bijoy2, Md Zahid Hasan3

  • 1Department of Software Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.

BMC Plant Biology
|July 3, 2026
PubMed
Summary

Related Concept Videos

Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

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This study introduces PD-ViCo, an AI model for diagnosing brinjal (eggplant) diseases. It achieves high accuracy, offering a reliable tool for farmers to improve crop yield and quality.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Brinjal (eggplant) production in South Asia is significantly impacted by diseases, affecting yield and quality.
  • Manual disease diagnosis is labor-intensive, subjective, and error-prone, highlighting the need for automated solutions.
  • Existing automated methods often struggle with data imbalance and generalization in real-world agricultural settings.

Purpose of the Study:

  • To develop and evaluate PD-ViCo, a novel, lightweight transformer-based model for accurate brinjal fruit disease classification.
  • To address challenges of data imbalance and improve model robustness using advanced techniques.
  • To provide an interpretable and practical tool for agricultural disease diagnosis.

Main Methods:

Keywords:
AgricultureBangladeshBrinjal diseaseEggplant diseaseExplainable AIPD-Vico modelTransformer

Related Experiment Videos

  • Developed a new dataset of 1,823 brinjal images covering five disease classes and healthy samples.
  • Employed a Simple Vision Transformer (ViT) enhanced with Patch Dropout and Contrastive Captioner (CoCa) methods.
  • Implemented extensive data preprocessing, class balancing (under-sampling/oversampling), and data augmentation.

    • Main Results:

    • PD-ViCo achieved 99.12% classification accuracy and 97.76% F1-score on imbalanced datasets.
    • The model demonstrated superior performance compared to standard ViT and Swin Transformer.
    • Explainability methods (Grad-CAM) confirmed model focus on relevant disease-affected regions.

    Conclusions:

    • PD-ViCo is a highly accurate, interpretable, and efficient model for multi-class brinjal disease diagnosis.
    • The model's robustness and generalization capabilities are enhanced by patch dropout and CoCa-style aggregation.
    • This research offers a valuable dataset and a practical decision-making protocol for agricultural stakeholders.