Determining the growth stages of sunflower plants using deep learning methods
| dc.contributor.author | Karahanlı, Gülay | |
| dc.contributor.author | Taşkın, Cem | |
| dc.date.accessioned | 2024-10-29T17:43:31Z | |
| dc.date.available | 2024-10-29T17:43:31Z | |
| dc.date.issued | 2024 | |
| dc.department | Tekirdağ Namık Kemal Üniversitesi | |
| dc.description.abstract | Thanks to the precision agriculture technologies developed in recent years, many processes such as irrigation, fertilization, spraying, weeding and harvesting of agricultural products can be done by autonomous systems. Especially in some plant species such as sunflower, when to apply these processes is largely decided according to the developmental stage of the plant. In this study, deep learning methods were used to classify the developmental stages of sunflower plants. Since the images taken with the drone are high resolution, each of them is divided into 6 equal parts, and then 8 classes are determined and the images belonging to each class are extracted. A data set consisting of 12800 images in total, 1600 in each class, was created. Six different deep learning models, namely AlexNet, InceptionV3, ResNet101, DenseNet121, MobileNet and Xception, were tested with Sgd, Adam and Rmsprop optimization methods and their performances were compared. In order to evaluate the success of the models correctly, the trained models were also tested on a second data set created with images taken from a different terrain and high success rates were obtained. In addition, a 7-class test set was created for images that could not be clearly determined at which stage the plant was in, and the success rates of the models were tested. It was observed that the success rate was very low for the images in the 7-8 intermediate class, and the filters used in the image processing techniques that would increase the success rate for this class were applied to the images, and the models were retrained and the results were evaluated. © 2024 Gazi Universitesi. All rights reserved. | |
| dc.identifier.doi | 10.17341/gazimmfd.1200615 | |
| dc.identifier.endpage | 1471 | |
| dc.identifier.issn | 1300-1884 | |
| dc.identifier.issue | 3 | en_US |
| dc.identifier.scopus | 2-s2.0-85185216794 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.startpage | 1455 | |
| dc.identifier.trdizinid | 1257909 | |
| dc.identifier.uri | https://doi.org/10.17341/gazimmfd.1200615 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/1257909 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.11776/12446 | |
| dc.identifier.volume | 39 | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | TR-Dizin | |
| dc.language.iso | tr | |
| dc.publisher | Gazi Universitesi | |
| dc.relation.ispartof | Journal of the Faculty of Engineering and Architecture of Gazi University | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | convolutional neural | |
| dc.subject | Deep learning | |
| dc.subject | image classification | |
| dc.subject | networks | |
| dc.subject | precision agriculture | |
| dc.subject | transfer learning | |
| dc.title | Determining the growth stages of sunflower plants using deep learning methods | |
| dc.title.alternative | Derin öğrenme yöntemleri kullanılarak ayçiçeği bitkisinin gelişim evrelerinin tespiti | |
| dc.type | Article |
