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Scopus YÖKSİS DOI Eşleşti SJR Q1

Classification of rice varieties with deep learning methods

Computers and Electronics in Agriculture · Ağustos 2021

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YÖKSİS Kayıtları
Classification of Rice Varieties with Deep Learning Methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
Classification of rice varieties with deep learning methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
Classification of Rice Varieties with Deep Learning Methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Classification of Rice Varieties with Deep Learning Methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
Classification of rice varieties with deep learning methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 10 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis
2011 ISSN: 01681699 SCI-Expanded
Prof. Dr. ŞAKİR TAŞDEMİR →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
2019 ISSN: 0168-1699 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS
2019 ISSN: 0168-1699 SCI-Expanded
Prof. Dr. MERT DEDEOĞLU →
Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques
2020 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Classification of Rice Varieties with Deep Learning Methods
2021 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi İLKAY ÇINAR →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi İLKAY ÇINAR →
Dry Bean Cultivars Classification Using Deep CNN Features and Salp Swarm Algorithm Based Extreme Learning Machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Classification of Rice Varieties with Deep Learning Methods
2021 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
2019 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi ESRA KAYA ERDOĞAN →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. İLKER ALİ ÖZKAN →

Makale Bilgileri

Dergi Computers and Electronics in Agriculture
ISSN01681699
Yayın TarihiAğustos 2021
Cilt / Sayfa187
DOI10.1016/j.compag.2021.106285
Scopus ID2-s2.0-85108321631
Özet Rice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used. Models were created by using Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms for the feature dataset and by using the Convolutional Neural Network (CNN) algorithm for the image dataset, and classification processes were performed. Statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate and false negative rate were calculated using the confusion matrix values of the models and the results of each model were given in tables. Classification successes from the models were achieved as 99.87% for ANN, 99.95% for DNN and 100% for CNN. With the results, it is seen that the models used in the study in the classification of rice varieties can be applied successfully in this field.

Yazarlar (3)

1
Murat Koklu
ORCID: 0000-0002-2737-2360
2
Ilkay Cinar
ORCID: 0000-0003-0611-3316
3
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241

Anahtar Kelimeler

Convolutional neural network Deep learning Performance evaluation Rice classification Rice varieties

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Computers and Electronics in Agriculture
Q1
SJR Skoru1,834
H-Index188
YayıncıElsevier B.V.
ÜlkeNetherlands
Agronomy and Crop Science (Q1)
Animal Science and Zoology (Q1)
Computer Science Applications (Q1)
Forestry (Q1)
Horticulture (Q1)
Dergi sayfasına git

Metrikler

202
Atıf
3
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

YT
YAVUZ SELİM TAŞPINAR
Doç. Dr.

Hızlı Erişim

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