A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture
Abstract Sustainable agriculture is an approach that involves adopting and developing agricultural practices to increase efficiency and preserve resources, both environmentally and economically. Jute is one of the primary sources of income grown in many countries. At this stage, increasing efficienc...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-00642-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849731842354184192 |
|---|---|
| author | Soner Kiziloluk Mucahit Karaduman Serpil Aslan Muhammed Yildirim Muhammad Attique Khan Fatimah Alhayan Yunyoung Nam |
| author_facet | Soner Kiziloluk Mucahit Karaduman Serpil Aslan Muhammed Yildirim Muhammad Attique Khan Fatimah Alhayan Yunyoung Nam |
| author_sort | Soner Kiziloluk |
| collection | DOAJ |
| description | Abstract Sustainable agriculture is an approach that involves adopting and developing agricultural practices to increase efficiency and preserve resources, both environmentally and economically. Jute is one of the primary sources of income grown in many countries. At this stage, increasing efficiency in jute production and protecting it from pests is essential. Detecting jute pests at an early stage will not only improve crop yield but also provide more income. In this paper, an artificial intelligence-based model was suggested to detect jute pests at an early stage. In this developed model, two different pre-trained models were used for feature extraction. To improve the performance of the developed model, the features obtained using the DarkNet-53 and DenseNet-201 models were combined. After this stage, the metaheuristic Mountain Gazelle Optimizer (MGO) was used, allowing the developed model to work faster and achieve more successful results. Feature selection was carried out using MGO; thus, more successful results were obtained with fewer, more compelling features. The proposed model was compared with six different models and five different classifiers accepted in the literature. In the developed model, 17 different jute pests were detected with 96.779% accuracy. The accuracy value achieved in the developed model is promising in successfully detecting jute pests. |
| format | Article |
| id | doaj-art-0ed5f02aff0a4e17bfd80ffdf021f430 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0ed5f02aff0a4e17bfd80ffdf021f4302025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-00642-xA novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agricultureSoner Kiziloluk0Mucahit Karaduman1Serpil Aslan2Muhammed Yildirim3Muhammad Attique Khan4Fatimah Alhayan5Yunyoung Nam6Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal UniversityDepartment of Software Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal UniversityDepartment of Software Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal UniversityDepartment of Computer Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal UniversityDepartment of AI, College of Computer Engineering and Science, Prince Mohammad Bin Fahd UniversityDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman UniversityEmotional and Intelligent Child Care Convergence Research Center, Soonchunhyang UniversityAbstract Sustainable agriculture is an approach that involves adopting and developing agricultural practices to increase efficiency and preserve resources, both environmentally and economically. Jute is one of the primary sources of income grown in many countries. At this stage, increasing efficiency in jute production and protecting it from pests is essential. Detecting jute pests at an early stage will not only improve crop yield but also provide more income. In this paper, an artificial intelligence-based model was suggested to detect jute pests at an early stage. In this developed model, two different pre-trained models were used for feature extraction. To improve the performance of the developed model, the features obtained using the DarkNet-53 and DenseNet-201 models were combined. After this stage, the metaheuristic Mountain Gazelle Optimizer (MGO) was used, allowing the developed model to work faster and achieve more successful results. Feature selection was carried out using MGO; thus, more successful results were obtained with fewer, more compelling features. The proposed model was compared with six different models and five different classifiers accepted in the literature. In the developed model, 17 different jute pests were detected with 96.779% accuracy. The accuracy value achieved in the developed model is promising in successfully detecting jute pests.https://doi.org/10.1038/s41598-025-00642-xJuteMountain gazelle optimizerOptimizationClassificationDeep learning |
| spellingShingle | Soner Kiziloluk Mucahit Karaduman Serpil Aslan Muhammed Yildirim Muhammad Attique Khan Fatimah Alhayan Yunyoung Nam A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture Scientific Reports Jute Mountain gazelle optimizer Optimization Classification Deep learning |
| title | A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture |
| title_full | A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture |
| title_fullStr | A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture |
| title_full_unstemmed | A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture |
| title_short | A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture |
| title_sort | novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture |
| topic | Jute Mountain gazelle optimizer Optimization Classification Deep learning |
| url | https://doi.org/10.1038/s41598-025-00642-x |
| work_keys_str_mv | AT sonerkiziloluk anovelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT mucahitkaraduman anovelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT serpilaslan anovelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT muhammedyildirim anovelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT muhammadattiquekhan anovelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT fatimahalhayan anovelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT yunyoungnam anovelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT sonerkiziloluk novelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT mucahitkaraduman novelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT serpilaslan novelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT muhammedyildirim novelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT muhammadattiquekhan novelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT fatimahalhayan novelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture AT yunyoungnam novelfeaturefusionandmountaingazelleoptimizerbasedframeworkfortherecognitionofjutepestsinsustainableagriculture |