Detection of Defects in Soybean Seeds by Extracting Deep Features with SqueezeNet
Yazarlar (4)
Murat Koklu Selçuk Üniversitesi, Türkiye
Öğr. Gör. Ramazan Kursun Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Selçuk Üniversitesi, Türkiye
Doç. Dr. Yavuz Selim Taspinar Selçuk Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/IDAACS58523.2023.10348939 | ||
| Kongre Adı | IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) | ||
| Kongre Tarihi | 07-09-2023 / 09-09-2023 | ||
| Basıldığı Ülke | Almanya | Basıldığı Şehir | Dortmund |
| Bildiri Linki | https://doi.org/10.1109/idaacs58523.2023.10348939 | ||
| UAK Araştırma Alanları |
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| Özet |
| A vital part of ensuring the quality of soybean products is detecting defects. The current study presents a five-category classification of soybean seed defects: broken, immature, intact, skin-damaged, and spotted soybean seeds. Our goal is to improve the overall quality of soybean products through accurately identifying and categorizing defects. Computer vision techniques and machine learning algorithms are combined comprehensively to achieve this goal. To begin with, soybean seed images are analyzed using the SqueezeNet model, a deep-learning architecture known for its efficiency in image analysis. Features extracted from soybeans indicate the types of defects they present and their key visual characteristics. Then, we applied three widely used machine learning algorithms, including Artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), to classify soybean seed images. A … |
| Anahtar Kelimeler |
| soybean | machine learning | ANN | LR | RF | feature exraction | classification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 6 |
| Google Scholar | 19 |