Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers
Sunn pest damage is one of the most crucial types of agricultural damage. Authorities and farmers are working together to find a cost-effective solution for separating the damaged crops from the healthy ones. This challenge can be tackled cost-effectively with emerging technology. Over time, the num...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Çanakkale Onsekiz Mart University
2024-12-01
|
| Series: | Journal of Advanced Research in Natural and Applied Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/4053616 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832088367354871808 |
|---|---|
| author | Melike Çolak Özgü Özkan Ali Berkol Nergis Pervan Akman Murat Ardıç Okan Sezer Nazife Gözde Ayter Arpacıoğlu Zekiye Budak Başçiftçi Murat Olgun |
| author_facet | Melike Çolak Özgü Özkan Ali Berkol Nergis Pervan Akman Murat Ardıç Okan Sezer Nazife Gözde Ayter Arpacıoğlu Zekiye Budak Başçiftçi Murat Olgun |
| author_sort | Melike Çolak |
| collection | DOAJ |
| description | Sunn pest damage is one of the most crucial types of agricultural damage. Authorities and farmers are working together to find a cost-effective solution for separating the damaged crops from the healthy ones. This challenge can be tackled cost-effectively with emerging technology. Over time, the number of researchers focusing on this problem by using various machine learning algorithms and image processing techniques has increased. This paper presents an approach using a recurrent neural networks-based transformer to identify different varieties of wheat grain that have been sunn pest-damaged and healthy. First, wheat grains were separated from each other using YOLOv8. Then, the dataset was enriched with different data augmentation techniques, and data-efficient vision transformers were used to classify sunn pest-damaged and healthy grains. Conversely, a high accuracy score of 98.61% was achieved on the augmented dataset while surpassing the accuracy score of 93.36% in the raw dataset. This paper's contributions to literature can be divided into three categories. In contrast to the previous research, perfectly shaped, broken, and half-wheat grains are used to better fit findings in real-life environments such as factory production lines. Moreover, this study employs a combination of augmentation techniques, implying that two separate augmentation techniques, texture-based and one morphological, were applied to the same image. Finally, no study in the available literature uses a vision transformer to classify healthy and sunned pest-damaged wheat grains. That leads to using a data-efficient vision transformer algorithm and achieving a high accuracy score of 98.61%. |
| format | Article |
| id | doaj-art-1f35e27ac1ed48c8b326caec9127b1df |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-1f35e27ac1ed48c8b326caec9127b1df2025-02-05T18:13:02ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952024-12-0110477178510.28979/jarnas.1512352453Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision TransformersMelike Çolak0https://orcid.org/0000-0002-7779-4756Özgü Özkan1https://orcid.org/0000-0001-8242-690XAli Berkol2https://orcid.org/0000-0002-3056-1226Nergis Pervan Akman3https://orcid.org/0000-0003-3241-6812Murat Ardıç4https://orcid.org/0000-0001-8734-3038Okan Sezer5https://orcid.org/0000-0001-7304-1346Nazife Gözde Ayter Arpacıoğlu6https://orcid.org/0000-0002-5121-4303Zekiye Budak Başçiftçi7https://orcid.org/0000-0002-4034-2537Murat Olgun8https://orcid.org/0000-0001-6981-4545BİTES Defence and AerospaceBİTES Defence and AerospaceBİTES Defence and AerospaceBİTES Defence and AerospaceESKISEHIR OSMANGAZI UNIVERSITYESKISEHIR OSMANGAZI UNIVERSITYESKISEHIR OSMANGAZI UNIVERSITYESKISEHIR OSMANGAZI UNIVERSITYESKISEHIR OSMANGAZI UNIVERSITYSunn pest damage is one of the most crucial types of agricultural damage. Authorities and farmers are working together to find a cost-effective solution for separating the damaged crops from the healthy ones. This challenge can be tackled cost-effectively with emerging technology. Over time, the number of researchers focusing on this problem by using various machine learning algorithms and image processing techniques has increased. This paper presents an approach using a recurrent neural networks-based transformer to identify different varieties of wheat grain that have been sunn pest-damaged and healthy. First, wheat grains were separated from each other using YOLOv8. Then, the dataset was enriched with different data augmentation techniques, and data-efficient vision transformers were used to classify sunn pest-damaged and healthy grains. Conversely, a high accuracy score of 98.61% was achieved on the augmented dataset while surpassing the accuracy score of 93.36% in the raw dataset. This paper's contributions to literature can be divided into three categories. In contrast to the previous research, perfectly shaped, broken, and half-wheat grains are used to better fit findings in real-life environments such as factory production lines. Moreover, this study employs a combination of augmentation techniques, implying that two separate augmentation techniques, texture-based and one morphological, were applied to the same image. Finally, no study in the available literature uses a vision transformer to classify healthy and sunned pest-damaged wheat grains. That leads to using a data-efficient vision transformer algorithm and achieving a high accuracy score of 98.61%.https://dergipark.org.tr/en/download/article-file/4053616sunn pestsunn pest detectionwheat cultivarswheat grain segmentationcrop quality |
| spellingShingle | Melike Çolak Özgü Özkan Ali Berkol Nergis Pervan Akman Murat Ardıç Okan Sezer Nazife Gözde Ayter Arpacıoğlu Zekiye Budak Başçiftçi Murat Olgun Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers Journal of Advanced Research in Natural and Applied Sciences sunn pest sunn pest detection wheat cultivars wheat grain segmentation crop quality |
| title | Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers |
| title_full | Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers |
| title_fullStr | Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers |
| title_full_unstemmed | Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers |
| title_short | Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers |
| title_sort | classifying sunn pest damaged and healthy wheat grains across different species with yolov8 and vision transformers |
| topic | sunn pest sunn pest detection wheat cultivars wheat grain segmentation crop quality |
| url | https://dergipark.org.tr/en/download/article-file/4053616 |
| work_keys_str_mv | AT melikecolak classifyingsunnpestdamagedandhealthywheatgrainsacrossdifferentspecieswithyolov8andvisiontransformers AT ozguozkan classifyingsunnpestdamagedandhealthywheatgrainsacrossdifferentspecieswithyolov8andvisiontransformers AT aliberkol classifyingsunnpestdamagedandhealthywheatgrainsacrossdifferentspecieswithyolov8andvisiontransformers AT nergispervanakman classifyingsunnpestdamagedandhealthywheatgrainsacrossdifferentspecieswithyolov8andvisiontransformers AT muratardıc classifyingsunnpestdamagedandhealthywheatgrainsacrossdifferentspecieswithyolov8andvisiontransformers AT okansezer classifyingsunnpestdamagedandhealthywheatgrainsacrossdifferentspecieswithyolov8andvisiontransformers AT nazifegozdeayterarpacıoglu classifyingsunnpestdamagedandhealthywheatgrainsacrossdifferentspecieswithyolov8andvisiontransformers AT zekiyebudakbasciftci classifyingsunnpestdamagedandhealthywheatgrainsacrossdifferentspecieswithyolov8andvisiontransformers AT muratolgun classifyingsunnpestdamagedandhealthywheatgrainsacrossdifferentspecieswithyolov8andvisiontransformers |