A Hybrid Model Approach Based on Swin Transformer and EfficientNetV2 for Maize Variety Classification
A Hybrid Model Approach Based on Swin Transformer and EfficientNetV2 for Maize Variety Classification
Official scientific asset for International Journal of Applied Methods in Electronics and Computers
Abstract
In this study, two different deep learning-based models were proposed for the classification of the maize varieties Chulpi Cancha, Indurata, and Rugosa. In the first stage, a single model was developed using the Swin Transformer architecture with an attention mechanism. This model was then integrated with EfficientNetV2 to create a hybrid structure. The developed models were tested on a dataset consisting of 1050 images with a fixed background and high resolution. The Swin Transformer model produced successful results with 99.37% accuracy, while the hybrid model achieved 100% test accuracy, accurately classifying all samples. The findings demonstrate that the Swin Transformer and EfficientNetV2-based hybrid architectures offer high discrimination power and generalization capacity in image-based classification of maize varieties. Future studies are recommended to conduct additional tests using images taken under different environmental conditions and larger datasets encompassing a wider range of varieties.
Author Affiliations
- Hüseyin Bulduk— Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Türkiyefingerprinthttps://orcid.org/0009-0009-5765-8659
- Kadir Sabancı— Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Türkiyefingerprinthttps://orcid.org/0000-0003-0238-9606
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