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Scopus YÖKSİS DOI Eşleşti SJR Q1

Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine

Computers and Electronics in Agriculture · Ocak 2023

YÖKSİS DOI Eşleşmesi Bulundu

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YÖKSİS Kayıtları
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
COMPUTERS AND ELECTRONICS IN AGRICULTURE · 2023 SCI-Expanded
Arş. Gör. MUSA DOĞAN →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
COMPUTERS AND ELECTRONICS IN AGRICULTURE · 2023 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
COMPUTERS AND ELECTRONICS IN AGRICULTURE · 2023 SCI-Expanded
Öğr. Gör. RAMAZAN KURŞUN →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
COMPUTERS AND ELECTRONICS IN AGRICULTURE · 2023 SCI-Expanded
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Dry Bean Cultivars Classification Using Deep CNN Features and Salp Swarm Algorithm Based Extreme Learning Machine
Computers and Electronics in Agriculture · 2023 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
Computers and Electronics in Agriculture · 2023 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →
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 TarihiOcak 2023
Cilt / Sayfa204
DOI10.1016/j.compag.2022.107575
Scopus ID2-s2.0-85144553220
Özet Since dry bean varieties have different qualities and economic values, their separation is of great importance in the field of agriculture. In recent years, the use of artificial intelligence-supported and image-based systems has become widespread for this process. This study aims to create a data set consisting of 14 classes in the detection of dry beans and to investigate the effectiveness of the hybrid structure of the extreme learning machine (ELM) model with GoogLeNet transfer learning on this dataset. At the same time, the salp swarm algorithm (SSA), which is one of the swarm intelligence algorithms, was used to test its applicability in ELM classifier by optimizing ELM parameters. The performance of these models was compared with ELM-based particle swarm optimization, harris hawks optimization, artificial bee colony, and traditional machine learning algorithms such as support vector machine and k-nearest neighbor. The suggested SSA-ELM model successfully classifies 14 different types of dry beans with a success rate of 91.43%. The comparable results demonstrate that the proposed hybrid model had better classification accuracy and performance metrics than traditional machine learning algorithms. In addition, it is seen that the use of image data, extraction of deep features, and classification with optimized ELM in the classification of dry beans have achieved comparable success in the literature.

Yazarlar (6)

1
Musa Dogan
2
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
3
Ilkay Cinar
ORCID: 0000-0003-0611-3316
4
Ramazan Kursun
ORCID: 0000-0002-6729-1055
5
Ilker Ali Ozkan
6
Murat Koklu
ORCID: 0000-0002-2737-2360

Anahtar Kelimeler

Classification Dry bean Extreme learning machine optimization Precision agriculture Transfer learning

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

58
Atıf
6
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

YT
YAVUZ SELİM TAŞPINAR
Doç. Dr.

Hızlı Erişim

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