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Integrating feature selection and explainable CNN for identification and classification of pests and beneficial insects

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Abstract

Reliable identification of agricultural pests and beneficial insects is crucial for sustainable crop protection and ecological balance, yet most vision-based models remain black boxes and require high-dimensional features. This paper proposes an explainable hybrid insect-classification framework that combines convolutional neural network (CNN) feature extraction with a dual–XAI feature selection strategy. SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI) are applied in parallel to rank handcrafted and CNN-derived features, and their intersection yields a compact, biologically meaningful subset for final classification. The selected features are evaluated using lightweight classifiers and a hybrid ensemble, enabling accurate inference under field variability. Experiments on a curated, balanced dataset of four classes (Colorado potato beetle, green peach aphid, seven-spot ladybird, and healthy leaves) collected under diverse lighting and background conditions achieve 96.7% overall accuracy, with precision, recall, and F1-scores all above 96%. Importantly, performance remains stable when reducing dimensionality, retaining (ge)90% accuracy using only the top 11 hybrid-selected features. These results demonstrate that integrating SHAP and PFI improves both robustness and interpretability, supporting practical deployment for automated pest monitoring and precision agriculture.

Data availability

The selected data sets are available from free and open access sources using the following link:https://doi.org/10. 34740/kaggle/dsv/12745007

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research and Libraries in Multimedia University for funding this research work through the Program for Supporting Publication in Top-Impact Journals.

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Authors and Affiliations

  1. Department of Biotechnology Engineering, Bioenvironmental Engineering Research Center (BERC), International Islamic University Malaysia, 50728, Kuala Lumpur, Malaysia

    Nibedita Deb

  2. Department of Electrical and Electronic Engineering, International University of Business Agriculture and Technology, Uttara, Dhaka, 1230, Bangladesh

    Tawfikur Rahman & Md. Moniruzzaman

  3. The Saudi Technology Development and Investment Company (Taqnia), 12211, Riyadh, Saudi Arabia

    Ameen Salem Bin Obadi

  4. Faculty of Artificial Intelligence and Engineering, Multimedia University, 63100, Cyberjaya, Malaysia

    Noorlindawaty Md. Jizat

  5. Space Science Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Malaysia

    Samir Salem Al-Bawri

  6. Department of Electrical and Electronic Engineering, Southeast University, Dhaka, 1208, Bangladesh

    Abdullah Al Mahfazur Rahman

  7. Department of Electronics & Communication Engineering, Faculty of Engineering & Petroleum, Hadhramout University, Mukalla, Hadhramout, Yemen

    Samir Salem Al-Bawri

Authors

  1. Nibedita Deb
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  2. Tawfikur Rahman
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  3. Md. Moniruzzaman
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  4. Ameen Salem Bin Obadi
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  5. Noorlindawaty Md. Jizat
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  6. Samir Salem Al-Bawri
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  7. Abdullah Al Mahfazur Rahman
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Contributions

N. D. and T. R. Conceptualization, Methodology, Software, Visualization, Formal Analysis, Writing-original draft and review & editing. M.M. and A.S.B.O. Conceptualization, Methodology, Resource, Supervision, Visualization, Writing- review & editing. N.M.J. Data curation, Software, Resource, Formal Analysis. S. S. A. and A. A. M. R. Methodology, Data Curation, Visualization, Validation and Writing (Review & Editing)

Corresponding authors

Correspondence to
Md. Moniruzzaman, Noorlindawaty Md. Jizat or Samir Salem Al-Bawri.

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Deb, N., Rahman, T., Moniruzzaman, M. et al. Integrating feature selection and explainable CNN for identification and classification of pests and beneficial insects.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32520-x

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  • DOI: https://doi.org/10.1038/s41598-025-32520-x

Keywords

  • Hybrid models
  • Feature selection
  • Pest detection
  • Beneficial insects
  • Machine learning
  • Agricultural informatics.

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