Towards precision agriculture: metaheuristic model compression for enhanced pest recognition
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 robustn...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08307-5 |
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| author | Sana Parez Norah Saleh Alghamdi Tahir Mahmood Waseem Ullah Muhammad Attique Khan Taha Houda Naqqash Dilshad |
| author_facet | Sana Parez Norah Saleh Alghamdi Tahir Mahmood Waseem Ullah Muhammad Attique Khan Taha Houda Naqqash Dilshad |
| author_sort | Sana Parez |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c6bbec2ce22e401c8d636df7fed045ae |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c6bbec2ce22e401c8d636df7fed045ae2025-08-20T03:45:19ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-08307-5Towards precision agriculture: metaheuristic model compression for enhanced pest recognitionSana Parez0Norah Saleh Alghamdi1Tahir Mahmood2Waseem Ullah3Muhammad Attique Khan4Taha Houda5Naqqash Dilshad6Department of Software, Sejong UniversityDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDivision of Electronics and Electrical Engineering, Dongguk UniversityMohamed bin Zayed University of Artificial IntelligenceCenter of AI, College of computer engineering and Science, Prince Mohammad Bin Fahd UniversityCenter of AI, College of computer engineering and Science, Prince Mohammad Bin Fahd UniversityDepartment of Computer Science and Engineering, Sejong UniversityAbstract 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.https://doi.org/10.1038/s41598-025-08307-5Convolution neural networkDisease recognitionDeep learningMachine learningPest detectionPrecision agriculture |
| spellingShingle | Sana Parez Norah Saleh Alghamdi Tahir Mahmood Waseem Ullah Muhammad Attique Khan Taha Houda Naqqash Dilshad Towards precision agriculture: metaheuristic model compression for enhanced pest recognition Scientific Reports Convolution neural network Disease recognition Deep learning Machine learning Pest detection Precision agriculture |
| title | Towards precision agriculture: metaheuristic model compression for enhanced pest recognition |
| title_full | Towards precision agriculture: metaheuristic model compression for enhanced pest recognition |
| title_fullStr | Towards precision agriculture: metaheuristic model compression for enhanced pest recognition |
| title_full_unstemmed | Towards precision agriculture: metaheuristic model compression for enhanced pest recognition |
| title_short | Towards precision agriculture: metaheuristic model compression for enhanced pest recognition |
| title_sort | towards precision agriculture metaheuristic model compression for enhanced pest recognition |
| topic | Convolution neural network Disease recognition Deep learning Machine learning Pest detection Precision agriculture |
| url | https://doi.org/10.1038/s41598-025-08307-5 |
| work_keys_str_mv | AT sanaparez towardsprecisionagriculturemetaheuristicmodelcompressionforenhancedpestrecognition AT norahsalehalghamdi towardsprecisionagriculturemetaheuristicmodelcompressionforenhancedpestrecognition AT tahirmahmood towardsprecisionagriculturemetaheuristicmodelcompressionforenhancedpestrecognition AT waseemullah towardsprecisionagriculturemetaheuristicmodelcompressionforenhancedpestrecognition AT muhammadattiquekhan towardsprecisionagriculturemetaheuristicmodelcompressionforenhancedpestrecognition AT tahahouda towardsprecisionagriculturemetaheuristicmodelcompressionforenhancedpestrecognition AT naqqashdilshad towardsprecisionagriculturemetaheuristicmodelcompressionforenhancedpestrecognition |