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Related Experiment Videos

TriAttnNet based deep learning model for automated cotton pest detection and disease classification.

Mohan Ajmeera1, P Chiranjeevi2, A Krishna Mohan3

  • 1Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Kakinada (JNTUK), Kakinada, Andhra Pradesh, India. amohanphd2020@gmail.com.

Scientific Reports
|June 26, 2026
PubMed
Summary

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Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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A new deep learning model accurately detects cotton pests and diseases using advanced image processing and data augmentation. This sustainable solution enhances precision agriculture and crop health monitoring.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Biotechnology

Background:

  • Cotton crop health is vital for global agriculture.
  • Pest detection and disease classification are challenging due to limited data and feature redundancy.
  • Effective monitoring systems are crucial for sustainable agriculture and food security.

Purpose of the Study:

  • To develop a robust deep learning framework for detecting cotton plant pests and classifying diseases.
  • To address challenges of limited datasets, class imbalance, and feature redundancy in agricultural image analysis.
  • To provide a computationally feasible and interpretable solution for precision agriculture.

Main Methods:

  • Image preprocessing using Gaussian blur filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE).
Keywords:
Cotton plant pest identification and categorizationHybrid mongoose ray chaotic optimizationSpatial generative adversarial networksTriAttnNet

Related Experiment Videos

  • Spa-GAN-based data augmentation for generating realistic synthetic samples.
  • Attention-Guided Multi-Scale Residual U-Net (AGMS-U-Net) for Region of Interest (RoI) segmentation.
  • TriAttnNet feature extractor with spatial, channel, and contextual attention.
  • Hybrid Mongoose Ray Chaotic Optimization (HMRCO) for parameter tuning.
  • Classification layer with focal loss.

    • Main Results:

    • The proposed TriAttnNet achieved 98.66% accuracy, 98.71% recall, and 98.81% F1-score.
    • Outperformed state-of-the-art models like EfficientNetB1-CBAM and BERT-ResNet-PSO.
    • Demonstrated effectiveness in overcoming data limitations and improving feature representation.

    Conclusions:

    • The developed deep learning model offers a powerful and interpretable solution for cotton pest and disease management.
    • The framework supports precision agriculture and sustainable cotton crop health monitoring.
    • The system is computationally feasible for practical implementation in agricultural settings.