Scopus
YÖKSİS Eşleşti
Optimized feature selection using gray wolf and particle swarm algorithms for corn seed image classification
Journal of Food Composition and Analysis · Eylül 2025
YÖKSİS Kayıtları
Optimized feature selection using gray wolf and particle swarm algorithms for corn seed image classification
Journal of Food Composition and Analysis · 2025 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ İLKAY ÇINAR →
Optimized Feature Selection Using Gray Wolf and Particle Swarm Algorithms for Corn Seed Image Classification
Journal of Food Composition and Analysis · 2025 SCI-Expanded
DOÇENT YAVUZ SELİM TAŞPINAR →
Optimized Feature Selection Using Gray Wolf and Particle Swarm Algorithms for Corn Seed Image Classification
Journal of Food Composition and Analysis · 2025 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Makale Bilgileri
DergiJournal of Food Composition and Analysis
Yayın TarihiEylül 2025
Cilt / Sayfa145
Scopus ID2-s2.0-105005876569
Özet
Corn, one of the agricultural products widely grown in the world, is an important nutrient for both humans and animals. Within the scope of this study, four corn cultivars (BT6470, Calipos, Es Armandi, and Hiva) licensed and produced by BIOTEK, were classified based on morphological, shape, and color features extracted from high-resolution RGB images. A dataset consisting of 14,469 individual seed images was constructed to support this classification task. A total of 106 features were extracted from each image and subsequently classified using three machine learning algorithms: Neural Network, Logistic Regression, and Random Forest. In the second stage, the Gray Wolf Optimizer (GWO) algorithm was applied to select and reduce the features to 44. In the third stage, 57 features were selected from the initial set using the Particle Swarm Optimization (PSO) algorithm. As a result, when the classification performances of all three stages were compared, it was found that the Neural Network was the most successful method with accuracy rates of 95.31 %, 95.09 % and 94.72 %, respectively. The results of the study show that the reduced number of features significantly reduces training and testing times. It is seen that the success performance does not change significantly in the classification made by reducing the optimization algorithms of the attribute numbers, and the calculation costs decrease.
Yazarlar (8)
1
Elham Tahsin Yasin
ORCID: 0000-0003-3246-6000
2
Ewa Ropelewska
ORCID: 0000-0001-8891-236X
3
Ramazan Kursun
ORCID: 0000-0002-6729-1055
4
Ilkay Cinar
ORCID: 0000-0003-0611-3316
5
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
6
Ali Yasar
7
Seyedali Mirjalili
ORCID: 0000-0003-3246-6000
8
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
Classification
Corn
Feature selection
Machine learning
Optimization
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
The National Institute of Horticultural Research
Skierniewice Poland
Torrens University Australia
Adelaide Australia