Citation

Deb, Nibedita and Rahman, Tawfikur and Moniruzzaman, Md. and Bin Obadi, Ameen Salem and Jizat, Noorlindawaty Md. and Al-Bawri, Samir Salem and Rahman, Abdullah Al Mahfazur (2025) Integrating feature selection and explainable CNN for identification and classification of pests and beneficial insects. Scientific Reports, 16 (1). ISSN 2045-2322

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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 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.

Item Type:	Article
Uncontrolled Keywords:	Hybrid models, Feature selection, Pest detection, Beneficial insects, Machine learning, Agricultural informatics
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	10 Feb 2026 07:24
Last Modified:	10 Feb 2026 07:24
URII:	http://shdl.mmu.edu.my/id/eprint/15313

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