A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture

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dc.contributor.authorKiziloluk, Soner
dc.contributor.authorKaraduman, Mucahit
dc.contributor.authorAslan, Serpil
dc.contributor.authorYildirim, Muhammed
dc.contributor.authorKhan, Muhammad Attique
dc.contributor.authorAlhayan, Fatimah
dc.contributor.authorNam, Yunyoung
dc.date.accessioned2026-06-19T06:39:36Z
dc.date.available2026-06-19T06:39:36Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractSustainable 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.
dc.description.sponsorshipNational Research Foundation of Korea(NRF) - Korea government(MSIT) [RS-2023-00218176]; Soonchunhyang University Research Fund [PNURSP2025R719]; Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2023-00218176) and the Soonchunhyang University Research Fund. This work was supported through Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R719), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
dc.identifier.doi10.1038/s41598-025-00642-x
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.orcid0000-0002-8087-4044
dc.identifier.pmid40414953
dc.identifier.scopus2-s2.0-105006447569
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1038/s41598-025-00642-x
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5701
dc.identifier.volume15
dc.identifier.wosWOS:001494976800011
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectJute
dc.subjectMountain Gazelle Optimizer
dc.subjectOptimization
dc.subjectClassification
dc.subjectDeep Learning
dc.titleA novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture
dc.typeArticle

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