Identification of haploid and diploid maize seeds using hybrid transformer model

Dönmez, Emrah; Kılıçarslan, Serhat; Közkurt, Cemil; Diker, Aykut; Demir, Fahrettin Burak; Elen, Abdullah

doi:10.1007/s00530-023-01174-y

Multimedia Systems

2023

DOI: 10.1007/s00530-023-01174-y

|View full text |Cite

Identification of haploid and diploid maize seeds using hybrid transformer model

Emrah Dönmez,

Serhat Kılıçarslan,

Cemil Közkurt

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Classification of hazelnut varieties based on bigtransfer deep learning model

Dönmez,

Kılıçarslan,

Diker

2024

Eur Food Res Technol

View full text Add to dashboard Cite

Hazelnut is an agricultural product that contributes greatly to the economy of the countries where it is grown. The human factor plays a major role in hazelnut classification. The typical approach involves manual inspection of each sample by experts, a process that is both labor-intensive and time-consuming, and often suffers from limited sensitivity. The deep learning techniques are extremely important in the classification and detection of agricultural products. Deep learning has great potential in the agricultural sector. This technology can improve product quality, increase productivity, and offer farmers the ability to classify and detect their produce more effectively. This is important for sustainability and efficiency in the agricultural industry. In this paper aims to the application of deep learning algorithms to streamline hazelnut classification, reducing the need for manual labor, time, and cost in the sorting process. The study utilized hazelnut images from three different varieties: Giresun, Ordu, and Van, comprising a dataset of 1165 images for Giresun, 1324 for Ordu, and 1138 for Van hazelnuts. This dataset is an open-access dataset. In the study, experiments were carried out on the determination of hazelnut varieties with BigTransfer (BiT)-M R50 × 1, BiT-M R101 × 3 and BiT-M R152 × 4 models. Deep learning models, including big transfer was employed for classification. The classification task involved 3627 nut images and resulted in a remarkable accuracy of 99.49% with the BiT-M R152 × 4 model. These innovative methods can also lead to patentable products and devices in various industries, thereby boosting the economic value of the country.

show abstract

Classification of hazelnut varieties based on bigtransfer deep learning model

Dönmez,

Kılıçarslan,

Diker

2024

Eur Food Res Technol

View full text Add to dashboard Cite

show abstract

Hybrid convolutional neural network and multilayer perceptron vision transformer model for wheat species classification task: E-ResMLP+

Dönmez

2024

Eur Food Res Technol

View full text Add to dashboard Cite

Wheat plant is one of the most basic food sources for the whole world. There are many species of wheat that differ according to the conditions of the region where they are grown. In this context, wheat species can exhibit different characteristics. Issues such as resistance to geographical conditions and productivity are at the forefront in this plant as in all other plants. The wheat species should be correctly distinguished for correct agricultural practice. In this study, a hybrid model based on the Vision Transformer (VT) approach and the Convolutional Neural Network (CNN) model was developed to classify wheat species. For this purpose, ResMLP architecture was modified and the EfficientNetV2b0 model was fine-tuned and improved. A hybrid transformer model has been developed by combining these two methods. As a result of the experiments, the overall accuracy performance has been determined as 98.33%. The potential power of the proposed method for computer-aided agricultural analysis systems is demonstrated.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Identification of haploid and diploid maize seeds using hybrid transformer model

Cited by 2 publications

References 37 publications

Classification of hazelnut varieties based on bigtransfer deep learning model

Classification of hazelnut varieties based on bigtransfer deep learning model

Hybrid convolutional neural network and multilayer perceptron vision transformer model for wheat species classification task: E-ResMLP+

Contact Info

Product

Resources

About