Konvolutional Nöral Ağ Kullanarak Hasta Elma Ağağı Yapraklarinin Segmentasyon

Loading...
Thumbnail Image

Date

2020

Journal Title

Journal ISSN

Volume Title

Publisher

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Organizational Unit
Airframe and Powerplant Maintenance
(2012)
The Atılım University Department of Airframe and Powerplant Maintenance has been offering Civil Aviation education in English since 2012. In an effort to provide the best level of education, ATILIM UNIVERSITY demonstrated its merit as a role model in Civil Aviation Education last year by being granted a SHY 147 certificate with the status of “Approved Aircraft Maintenance Training Institution” by the General Directorate of Civil Aviation. The SHY 147 is a certificate for Approved Aircraft Maintenance Training Institutions. It is granted to institutions where training programs have undergone inspection, and the quality of the education offered has been approved by the General Directorate of Civil Aviation. With our Civil Aviation Training Center at Esenboğa Airport (our hangar), and the two Cessna-337 planes with double piston engines both of which are fully operational, as well our Beechcraft C90 Kingait plaine with double Turboprop engines, Atılım University is an institution to offer hands-on technical training in civil aviation, and one that strives to take the education it offers to the extremes in terms of technology. The Atılım university Graduate School Department of Airframe and Powerplant Maintenance is a fully-equipped civil aviation school to complement its theoretical education with hands-on training using planes of various kinds. Even before their graduation, most of our students are hired in Turkey’s most prestigious institutions in such a rapidly-developing sector. We are looking forward to welcoming you at this modern and contemporary institution for your education in civil aviation.

Journal Issue

Events

Abstract

Tarım alanında, uzmanın gözü hastalığı erken bir aşamada tanımlayamayabilir veya doğru bir şekilde teşhis edemeyebilir. Bitki hastalığının yanlış teşhisi genellikle yanlış tedavinin seçilmesine ve bu da mahsulün kaybına neden olur. Bu nedenle, hastalıklı yaprağın otomatik segmentasyon sistemi bu sorunu çözmek için son derece gereklidir. Bu tez Bitki Patolojisi 2020 segmentasyonunda derin öğrenme nin cesaretini görüntüler - FGVC7 veri seti elma kabuğu gibi birden fazla elma foliar hastalığı belirtileri yüksek çözünürlüklü renkli görüntüler içeren, sedir elma pas, ve sağlıklı yapraklar. Önerilen segmentasyon algoritması, U-Net ve ResNet olmak üzere iki farklı mimari kullanılarak yapılan anlamsal segmentasyon yaklaşımıdır. Her iki ağın sonuçları Pixel Accuracy, IoU, F1-Score ve Recall ölçümleri kullanılarak değerlendirilmiş ve karşılaştırma ResNet'in bu amaca yönelik verimliliğini göstermiştir.
In the field of agriculture, the expert's eye might not be able to identify or correctly diagnose the disease at an early stage. The misdiagnosis of plant disease often leads to choosing the wrong treatment and this leads to losing the crops. Therefore, an automatic segmentation system of the diseased leaf is highly required to solve this issue. This thesis displays the prowess of deep learning in the segmentation of Plant Pathology 2020 - FGVC7 dataset that includes high-resolution coloured images of multiple apple foliar disease symptoms such as apple scab, cedar apple rust, and healthy leaves. The proposed segmentation algorithm is the semantic segmentation approach by using two different architectures U-Net and ResNet. The results of both networks have been evaluated by using Pixel Accuracy, IoU, F1-Score, and Recall metrics, and the comparison showed the efficiency of ResNet for this purpose.

Description

Keywords

Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering

Turkish CoHE Thesis Center URL

Fields of Science

Citation

WoS Q

Scopus Q

Source

Volume

Issue

Start Page

0

End Page

77