Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms
European Food Research and Technology · Ekim 2023
YÖKSİS Kayıtları
Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms
European Food Research and Technology · 2023 SCI-Expanded
Öğr. Gör. RAMAZAN KURŞUN →
Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms
European Food Research and Technology · 2023 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U‐Net and classification using deep learning algorithms
European Food Research and Technology · 2023 SCI
Prof. Dr. KUBİLAY KURTULUŞ BAŞTAŞ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded
Dr. Öğr. Üyesi İRFAN ÖZER →
The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded 14 atıf
Dr. Öğr. Üyesi İRFAN ÖZER →
The effect of various types of poultry pre and post rigor meats on emulsification capacity water holding capacity and cooking loss
2005 ISSN: 1438-2377 SSCI 15 atıf
Prof. Dr. CEMALETTİN SARIÇOBAN →
The effect of irrigation and harvest time on bioactive properties of olive fruits issued from some olive varieties grown in Mediterranean region
2020 ISSN: 1438-2377 SCI
Doç. Dr. NURHAN USLU →
The effect of irrigation and harvest time on bioactive properties
of olive fruits issued from some olive varieties grown in Mediterranean
region
2020 ISSN: 1438-2377 SCI-Expanded
Prof. Dr. MEHMET MUSA ÖZCAN →
Volatile profile evolution and sensory evaluation of traditional skinbag Tulum cheeses manufactured in Karaman mountainous region of Turkey during ripening
2021 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. TALHA DEMİRCİ →
Computer Vision Classification of Dry Beans (Phaseolus Vulgaris L.) Based on Deep Transfer Learning Techniques
2022 ISSN: 1438-2377 SCI-Expanded Q2
Arş. Gör. MUSA DOĞAN →
The influence of decoction and infusion methods and times on antioxidant activity, caffeine content and phenolic compounds of coffee brews
2022 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. NURHAN USLU →
A New Hybrid Model for Classification of Corn Using Morphological Properties
2023 ISSN: 1438-2377 SCI-Expanded Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded Q2
Dr. Öğr. Üyesi İRFAN ÖZER →
A review: benefit and bioactive properties of olive (Olea europaea L.) leaves
2016 ISSN: 1438-2377 SCI-Expanded
Prof. Dr. MEHMET MUSA ÖZCAN →
Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques
2022 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. İLKER ALİ ÖZKAN →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. ADEM GÖLCÜK →
Benchmarking analysis of CNN models for bread wheat varieties
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. ALİ YAŞAR →
Classification of Deep Image Features of Lentil Varieties with Machine Learning Techniques
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
A New Hybrid Model for Classification of Corn Using Morphological Properties
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Detection of fish freshness using artificial intelligence methods
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. ALİ YAŞAR →
Makale Bilgileri
ISSN14382377
Yayın TarihiEkim 2023
Cilt / Sayfa249 · 2543-2558
Scopus ID2-s2.0-85162959286
Özet
Detecting plant diseases is a challenging and time-consuming task that requires expertise and laboratory conditions. Deep learning methods have been proposed as a solution to this problem, and their effectiveness in plant disease detection has become a popular research topic in recent years. This study aimed to investigate the performance of the U-Net architecture, which has been successful in medical image segmentation, in the segmentation of agricultural images. Sixty images, including angular leaf spot and bean rust diseases commonly found in bean plants, were used in the study. The images were segmented with U-Net, and then, in the first stage, only the images containing diseases were classified. In the second stage, classification was performed using raw images. Deep learning methods VGG16, AlexNet, MobileNet-v2, and DenseNet201 were used for the classifications. The results showed that the classification accuracy was higher for the segmented images than for the raw images. The highest accuracy rate, 100%, was achieved with DenseNet201 in the classification carried out by removing the segmented diseased regions. Using the U-Net architecture, which has demonstrated good performance with relatively few medical images, promising results were achieved in segmenting plant diseases. A software was developed to obtain only the images of the diseased areas by overlapping the original images with the segmented images. The proposed end-to-end system achieved higher classification accuracy by focusing deep learning architectures only on the desired regions. Finally, 100% classification accuracy was achieved with the DenseNet201 architecture using only segmented diseased images.
Yazarlar (3)
1
Ramazan Kursun
ORCID: 0000-0002-6729-1055
2
Kubilay Kurtuluş Baştaş
3
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
Angular leaf spot
Bean rust
Deep learning
Dry bean disease
U-Net
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
European Food Research and Technology
Q1
SJR Skoru0,744
H-Index131
YayıncıSpringer Science and Business Media Deutschland GmbH
ÜlkeGermany
Food Science (Q1)
Industrial and Manufacturing Engineering (Q1)
Biochemistry (Q2)
Biotechnology (Q2)
Chemistry (miscellaneous) (Q2)
Metrikler
30
Atıf
3
Yazar
5
Anahtar Kelime