Scopus
YÖKSİS Eşleşti
Fast and Accurate Classification of Corn Varieties Using Deep Learning With Edge Detection Techniques
Journal of Food Science · Temmuz 2025
YÖKSİS Kayıtları
Fast and Accurate Classification of Corn Varieties Using Deep Learning with Edge Detection Techniques
Journal of Food Science · 2025 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Makale Bilgileri
DergiJournal of Food Science
Yayın TarihiTemmuz 2025
Cilt / Sayfa90
Scopus ID2-s2.0-105011957758
Özet
Correct grading of corn for food production raises the standard of products offered to consumers and maintains product quality. Classification ensures optimal storage and processing conditions. As a result, losses are minimized, costs are reduced, and agriculture becomes more sustainable. When dealing with huge data, classification needs to be done quickly and accurately. A faster way of achieving the same classification success was explored in this study. Deep learning models ResCNN, DAG-Net, and ResNet-18 were used to classify three corn varieties named Chulpi Cancha, Indurata, and Rugosa. With 1050 corn images, the classification process was carried out. A total of three datasets were obtained using Canny edge detection algorithm (CEDA), Sobel edge detection algorithm (SEDA), and normal color images (CI). Based on experimental studies with CI, the accuracy values of 0.9952, 1, 0.9952; 0.9933, 1, 0.9933; and 0.9952, 1, 0.9952 were obtained for Chulpi Cancha, Indurata, Rugosa corn varieties using ResCNN, DAG-Net, and ResNet-18 deep learning models, respectively. With the images generated by CEDA, the accuracy values for Chulpi Cancha, Indurata, and Rugosa corn varieties were 0.9904, 1, 0.9904; 0.9952, 0.9990, 0.9961; and 0.9952, 1, 0.9952, respectively. Using ResCNN, DAG-Net, and ResNet-18 deep learning models, accuracy values were obtained. Based on the images obtained through SEDA, the accuracy values for Chulpi Cancha, Indurata, and Rugosa corn varieties were 0.9933, 1, 0.9933; 0.9952, 1, 0.9952; and 0.9952, 1, 0.9952 using ResCNN, DAG-Net, and ResNet-18 deep learning models, respectively. ResCNN, DAG-Net, and ResNet-18 models trained faster than CI.
Yazarlar (2)
1
Emre Avuçlu
2
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
corn classification
deep learning
Kurumlar
Aksaray Üniversitesi
Aksaray Turkey
Selçuk Üniversitesi
Selçuklu Turkey