A New Hybrid Model for Classification of Corn Using Morphological Properties
Yazarlar (3)
Doç. Dr. Emre Avuçlu Aksaray Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
Doç. Dr. Murat Köklü Selçuk Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | European Food Research and Technology (Q2) | ||
| Dergi ISSN | 1438-2377 Dergi Bilgileri (2023) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2023 |
| Cilt / Sayı / Sayfa | 249 / 3 / 835–847 | DOI | 10.1007/s00217-022-04181-x |
| Makale Linki | https://link.springer.com/article/10.1007/s00217-022-04181-x | ||
| UAK Araştırma Alanları |
Yapay Zeka
|
||
| Özet |
| Automated classification of corn is important for corn sorting in intelligent agriculture. Corn classification process is a necessary and accurate process in many places in the world today. Correct corn classification is important to identify product quality and to distinguish good from bad. In this study, a hybrid model was proposed to classify the 3 corn species belonging to the Zea mays family. In the hybrid model, 12 different morphological features of corn were obtained. These morphological features were used for the classification process in the hybrid model created using machine learning (ML) algorithms. When morphological features were given as input to ML algorithms for normal classification, the test score was 96.66% for Decision Tree (DT), 97.32% for Random Forest (RF) and 96.66% for Naive Bayes (NB). With the proposed hybrid model, this rate has reached 100% test score in all three algorithms. Test … |
| Anahtar Kelimeler |
| Computer vision | Corn classification | Image collection system | Machine learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 9 |
| Scopus | 14 |
| Google Scholar | 23 |