Classifying Weed Development Stages Using Deep Learning Methods: Classifying Weed Development Stages with DenseNET, Xception, SqueezeNET, GoogleNET, EfficientNET CNN Models Using ROI Images

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dc.authoridUludağ, Ahmet / 0000-0002-7137-2616
dc.contributor.authorÇiçek, Yasin
dc.contributor.authorGülbandılar, Eyyüp
dc.contributor.authorÇıray, Kadir
dc.contributor.authorUludağ, Ahmet
dc.date.accessioned2025-05-29T02:54:06Z
dc.date.available2025-05-29T02:54:06Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe control of harmful weeds holds a significant place in the cultivation of agricultural products. A crucial criterion in this control process is identifying the development stages of the weeds. The technique to be used is determined based on the weed's growth stage. This study addresses the application of deep learning methods in classifying growth stages using images of various weed species to predict their development periods. Four different weed species, obtained from seeds collected in Turkey-Afyonkarahisar-Sinanpaşa Plain, were used in the study. The images were captured with a Nikon D7000 camera equipped with three different lenses, and the ROI extraction was performed using Lifex software. Using these ROI images, deep learning models such as DenseNet, EfficientNet, GoogleNet, Xception, and SqueezeNet were evaluated. Performance metrics including accuracy, F1 score, precision, and recall were employed. In the 4-class dataset with ROI annotations, DenseNet and Xception achieved an accuracy of 86.57%, while EfficientNet demonstrated the highest performance with an accuracy of 89.55%. Following the initial tests, it was concluded that classes 3 and 4 exhibited extreme similarity caused most of the prediction errors. Merging the said classes significantly increased the accuracy and F1 scores across all models. In image classification tests, SqueezeNet and GoogleNet demonstrated the shortest processing times. However, while EfficientNet lagged slightly behind these models in terms of speed, it exhibited superior accuracy. In conclusion, although the use of ROI improved classification performance, class merging strategies resulted in a more significant performance enhancement. © (2025), (Science and Information Organization). All Rights Reserved.
dc.identifier.doi10.14569/IJACSA.2025.0160263
dc.identifier.endpage626
dc.identifier.issn2158-107X
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85219719408
dc.identifier.scopusqualityQ3
dc.identifier.startpage619
dc.identifier.urihttps://doi.org/10.14569/IJACSA.2025.0160263
dc.identifier.urihttps://hdl.handle.net/20.500.12428/29941
dc.identifier.volume16
dc.identifier.wosWOS:001441820400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Sciences
dc.language.isoen
dc.publisherScience and Information Organization
dc.relation.ispartofInternational Journal of Advanced Computer Science and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20250529
dc.subjectclassification
dc.subjectDeep learning
dc.subjectDenseNET
dc.subjectEfficientNET
dc.subjectGoogleNET
dc.subjectROI
dc.subjectSqueezeNET
dc.subjectweed development stages
dc.subjectXception
dc.titleClassifying Weed Development Stages Using Deep Learning Methods: Classifying Weed Development Stages with DenseNET, Xception, SqueezeNET, GoogleNET, EfficientNET CNN Models Using ROI Images
dc.typeArticle

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