Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/5006
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ortataş, Fatma Nur | - |
dc.contributor.author | Özkaya, Umut | - |
dc.contributor.author | Şahin, Muhammet Emin | - |
dc.contributor.author | Ulutaş, Hasan | - |
dc.date.accessioned | 2024-01-23T09:29:40Z | - |
dc.date.available | 2024-01-23T09:29:40Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 0941-0643 | - |
dc.identifier.issn | 1433-3058 | - |
dc.identifier.uri | https://doi.org/10.1007/s00521-023-09320-3 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/5006 | - |
dc.description.abstract | The main objective of this study is to develop a method for the automated detection and classification of weeds and sugar beets. Precision agriculture is an essential area of research that aims to optimize farming practices and reduce the use of harmful chemicals. For this purpose, the Faster RCNN and Federating Learning (FL)-based ensemble models were utilized to classify a specific dataset. In the first stage of the study, feature extraction is performed from the images in the dataset and classified by machine learning algorithms. Then, classification is carried out with the help of FL based deep learning ensemble models. Within the scope of the study, grid search is used for hyperparameter optimization and the results are obtained by a tenfold cross-validation method. Among all tested algorithms, the FL-based ensemble model constructed using the ResNet50 model exhibited the highest accuracy rate of 99%. This system has the potential to significantly reduce the use of herbicides and other chemicals in agricultural practices, promoting a more sustainable form of agriculture. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer London Ltd | en_US |
dc.relation.ispartof | Neural Computing & Applications | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Weed detection | en_US |
dc.subject | Ensemble model | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Image processing | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Neural-Networks | en_US |
dc.subject | Identification | en_US |
dc.title | Sugar beet farming goes high-tech: a method for automated weed detection using machine learning and deep learning in precision agriculture | en_US |
dc.type | Article | en_US |
dc.type | Article; Early Access | en_US |
dc.identifier.doi | 10.1007/s00521-023-09320-3 | - |
dc.identifier.scopus | 2-s2.0-85179681027 | en_US |
dc.department | KTÜN | en_US |
dc.authorwosid | ulutaş, hasan/JKT-2129-2023 | - |
dc.identifier.wos | WOS:001125804200001 | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57362179800 | - |
dc.authorscopusid | 57191610477 | - |
dc.authorscopusid | 57196724125 | - |
dc.authorscopusid | 57483513600 | - |
item.fulltext | No Fulltext | - |
item.openairetype | Article | - |
item.openairetype | Article; Early Access | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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