Scopus
YÖKSİS Eşleşti
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
Computers and Electronics in Agriculture · Ocak 2023
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
DOÇENT İLKER ALİ ÖZKAN →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
COMPUTERS AND ELECTRONICS IN AGRICULTURE · 2023 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ 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
ÖĞRETİM GÖREVLİSİ 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
ARAŞTIRMA GÖREVLİSİ 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
DOÇENT 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
DOÇENT MURAT KÖKLÜ →
Makale Bilgileri
DergiComputers and Electronics in Agriculture
Yayın TarihiOcak 2023
Cilt / Sayfa204
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
Metrikler
34
Atıf
6
Yazar
5
Anahtar Kelime