Identification of wheat seeds from bran layer using optical microscopy and deep learning
BIOLOGICAL DIVERSITY AND CONSERVATION, vol.18, no.3, pp.349-352, 2025 (TRDizin)
- Publication Type: Article / Article
- Volume: 18 Issue: 3
- Publication Date: 2025
- Doi Number: 10.46309/biodicon.2025.1656264
- Journal Name: BIOLOGICAL DIVERSITY AND CONSERVATION
- Journal Indexes: TR DİZİN (ULAKBİM), Asos İndeks
- Page Numbers: pp.349-352
- Eskisehir Osmangazi University Affiliated: Yes
Abstract
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.