Scopus
YÖKSİS Eşleşti
Classification of rice varieties with deep learning methods
Computers and Electronics in Agriculture · Ağustos 2021
YÖKSİS Kayıtları
Classification of Rice Varieties with Deep Learning Methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
DOÇENT YAVUZ SELİM TAŞPINAR →
Classification of Rice Varieties with Deep Learning Methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Classification of rice varieties with deep learning methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Classification of Rice Varieties with Deep Learning Methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ İLKAY ÇINAR →
Classification of rice varieties with deep learning methods
Computers and Electronics in Agriculture · 2021 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ İLKAY ÇINAR →
Makale Bilgileri
DergiComputers and Electronics in Agriculture
Yayın TarihiAğustos 2021
Cilt / Sayfa187
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
Metrikler
100
Atıf
3
Yazar
5
Anahtar Kelime