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Towards precision agriculture: metaheuristic model compression for enhanced pest recognition
Parez, Sana Alghamdi, Norah Saleh Mahmood, Tahir Ullah, Waseem Khan, Muhammad Attique Houda, Taha Dilshad, Naqqash
Parez, Sana
Alghamdi, Norah Saleh
Mahmood, Tahir
Ullah, Waseem
Khan, Muhammad Attique
Houda, Taha
Dilshad, Naqqash
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Research Projects
Organizational Units
Journal Issue
Abstract
Crop diseases and insect pests pose significant challenges to agricultural productivity, often resulting in considerable yield losses. Traditional pest recognition methods, which rely heavily on manual feature extraction, are not only time consuming and labor intensive but also lack robustness in diverse conditions. While deep learning (DL) models have improved performance over conventional approaches, they typically suffer from high computational demands and large model sizes, limiting their real-world applicability. This study proposes a novel and efficient DL-based framework for the accurate identification and classification of crop pests and diseases. The core of this approach integrates InceptionV3 as a backbone feature extractor to capture rich and discriminative features, enhanced further using a channel attention (CA) mechanism for feature refinement. To reduce model complexity and improve deployment feasibility, a metaheuristic optimization algorithm was incorporated that significantly reduces computational overhead without compromising performance. The proposed model was rigorously evaluated on the CropDP-181 dataset, outperforming several state-of-the-art methods in both classification accuracy and computational efficiency. Notably, the proposed method achieved a precision of 0.932, recall of 0.891, F1-score of 0.911, an overall accuracy of 88.50%, and an MCC of 0.816 demonstrating its effectiveness and practical potential in real-time agricultural monitoring systems.
Citation
S. Parez et al., “Towards precision agriculture: metaheuristic model compression for enhanced pest recognition,” Sci Rep, vol. 15, no. 1, pp. 1–13, Dec. 2025, doi: 10.1038/S41598-025-08307-5
Source
Scientific Reports
Conference
Keywords
Convolution neural network, Deep learning, Disease recognition, Machine learning, Pest detection, Precision agriculture
Subjects
Source
Publisher
Springer Nature