Identification of wheat seeds from bran layer using optical microscopy and deep learning
BIOLOGICAL DIVERSITY AND CONSERVATION, cilt.18, sa.3, ss.349-352, 2025 (TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 18 Sayı: 3
- Basım Tarihi: 2025
- Doi Numarası: 10.46309/biodicon.2025.1656264
- Dergi Adı: BIOLOGICAL DIVERSITY AND CONSERVATION
- Derginin Tarandığı İndeksler: TR DİZİN (ULAKBİM), Asos İndeks
- Sayfa Sayıları: ss.349-352
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
Abstract
Purpose: This study aims to automate the identification of grain varieties and select the most suitable wheat genotypes for specific ecological conditions using Artificial Intelligence (AI)-based systems. The goal is to facilitate high-yield and high-quality production through pre-sowing analysis.
Method: Seeds from nine wheat genotypes with different qualities were used, and cross-sections of the wheat genotypes were photographed under a light microscope to create a specialized dataset. A Convolutional Neural Network (CNN)-based automated wheat identification framework was then proposed, utilizing both shallow and deep architectures.
Findings: The experiments confirm that CNN-based methods are highly effective in extracting distinctive features from wheat bran and accurately identifying wheat seed varieties.
Conclusion: The research successfully distinguished nine varieties and found that a simpler model (ResNet18) outperformed deeper networks, offering a practical solution for agricultural verification.