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Updated: May 1, 2026

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Large scale multi-class pest image classification using structurally adapted DenseNet architecture.

Neetu Agrawal1, Mehul Mahrishi1, Mukesh Kumar Gupta2

  • 1Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur, Rajasthan, India.

Scientific Reports
|April 29, 2026
PubMed
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Accurate crop pest identification is crucial for reducing pesticide use and supporting environmental conservation. This study introduces a deep learning model that achieves high accuracy in classifying pest images, aiding sustainable agriculture.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Environmental Science

Background:

  • Reducing pesticide use is vital for environmental conservation, aligning with UN Sustainable Development Goals (SDGs) 12 and 13.
  • Accurate pest identification is key to targeted pest control, minimizing unnecessary pesticide application.
  • Traditional pest identification methods are resource-intensive and require expert knowledge, hindering efficiency.

Purpose of the Study:

  • To develop an efficient and accurate deep learning model for multi-class crop pest image classification.
  • To adapt and fine-tune a DenseNet model for improved pest identification performance.
  • To address dataset imbalance issues in pest image datasets for unbiased model outcomes.

Main Methods:

  • A structurally adapted DenseNet model was developed for fine-grained pest image classification.

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Published on: December 15, 2023

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  • Hyperparameter tuning, including dense blocks and transition layers, was performed for model optimization.
  • The model was trained and validated on three datasets, including the large-scale IP102 dataset (75,000+ images, 102 species).

    • Main Results:

    • The proposed DenseNet model achieved 82.69% accuracy and an 81.45% F1 score on the IP102 dataset.
    • The model demonstrated consistent performance across different datasets.
    • Dataset imbalance was addressed, leading to more robust classification outcomes.

    Conclusions:

    • The developed deep learning model offers an effective solution for automated crop pest identification.
    • This approach supports targeted pest management strategies, contributing to reduced pesticide reliance.
    • The research advances automated pest classification, complementing existing ecological and conservation efforts.