Identification of wheat seeds from bran layer using optical microscopy and deep learning

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Anagün Y., Işık Ş., Olgun M., Sezer O.

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.