Cocoa Ripeness Classification Using Vision Transformer
The quality of manual methods for assessing the ripeness of cocoa pods is subjective and varies from one person to another because of the intense labor required and variation of light and background conditions within the field. This research implemented an automated classification approach for coco...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2025-06-01
|
| Series: | Journal of Applied Engineering and Technological Science |
| Subjects: | |
| Online Access: | http://journal.yrpipku.com/index.php/jaets/article/view/6663 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850224275457310720 |
|---|---|
| author | Febryanti Sthevanie Untari Novia Wisesty Gia Septiana Wulandari Kurniawan Nur Ramadhani |
| author_facet | Febryanti Sthevanie Untari Novia Wisesty Gia Septiana Wulandari Kurniawan Nur Ramadhani |
| author_sort | Febryanti Sthevanie |
| collection | DOAJ |
| description |
The quality of manual methods for assessing the ripeness of cocoa pods is subjective and varies from one person to another because of the intense labor required and variation of light and background conditions within the field. This research implemented an automated classification approach for cocoa ripeness classification utilizing Vision Transformer (ViT) with Shifted Patch Tokenization (SPT) and Locality Self Attention (LSA) to improve classification accuracy. The model proposed in this research achieved an accuracy of 82.65% and a macro F1 score of 82.71 on the exam with 1,559 images captured under varying illumination backgrounds and complex scenes. The model also proved better than baseline CNN architectures such as VGG, MobileNet, and ResNet in identifying visually progressive stages of ripeness and demonstrated greater generalization in cocoa ripeness classification. The findings of this research indicate the benefits of reducing manual intervention with careful inspection without compromising quality assurance standards in cocoa production. This work demonstrates new ways of applying transformer models to address computer vision problems in agriculture which is a step towards precision and smart farming.
|
| format | Article |
| id | doaj-art-698c435b0ea547feaae31a60a3f17142 |
| institution | OA Journals |
| issn | 2715-6087 2715-6079 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj-art-698c435b0ea547feaae31a60a3f171422025-08-20T02:05:40ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792025-06-016210.37385/jaets.v6i2.6663Cocoa Ripeness Classification Using Vision TransformerFebryanti Sthevanie0Untari Novia Wisesty1Gia Septiana Wulandari2Kurniawan Nur Ramadhani3Telkom UniversityTelkom UniversityTelkom UniversityTelkom University The quality of manual methods for assessing the ripeness of cocoa pods is subjective and varies from one person to another because of the intense labor required and variation of light and background conditions within the field. This research implemented an automated classification approach for cocoa ripeness classification utilizing Vision Transformer (ViT) with Shifted Patch Tokenization (SPT) and Locality Self Attention (LSA) to improve classification accuracy. The model proposed in this research achieved an accuracy of 82.65% and a macro F1 score of 82.71 on the exam with 1,559 images captured under varying illumination backgrounds and complex scenes. The model also proved better than baseline CNN architectures such as VGG, MobileNet, and ResNet in identifying visually progressive stages of ripeness and demonstrated greater generalization in cocoa ripeness classification. The findings of this research indicate the benefits of reducing manual intervention with careful inspection without compromising quality assurance standards in cocoa production. This work demonstrates new ways of applying transformer models to address computer vision problems in agriculture which is a step towards precision and smart farming. http://journal.yrpipku.com/index.php/jaets/article/view/6663Cocoa Ripeness ClassificationVision TransformerShifted Patch TokenizationLocality Self AttentionAgricultural Computer Vision |
| spellingShingle | Febryanti Sthevanie Untari Novia Wisesty Gia Septiana Wulandari Kurniawan Nur Ramadhani Cocoa Ripeness Classification Using Vision Transformer Journal of Applied Engineering and Technological Science Cocoa Ripeness Classification Vision Transformer Shifted Patch Tokenization Locality Self Attention Agricultural Computer Vision |
| title | Cocoa Ripeness Classification Using Vision Transformer |
| title_full | Cocoa Ripeness Classification Using Vision Transformer |
| title_fullStr | Cocoa Ripeness Classification Using Vision Transformer |
| title_full_unstemmed | Cocoa Ripeness Classification Using Vision Transformer |
| title_short | Cocoa Ripeness Classification Using Vision Transformer |
| title_sort | cocoa ripeness classification using vision transformer |
| topic | Cocoa Ripeness Classification Vision Transformer Shifted Patch Tokenization Locality Self Attention Agricultural Computer Vision |
| url | http://journal.yrpipku.com/index.php/jaets/article/view/6663 |
| work_keys_str_mv | AT febryantisthevanie cocoaripenessclassificationusingvisiontransformer AT untarinoviawisesty cocoaripenessclassificationusingvisiontransformer AT giaseptianawulandari cocoaripenessclassificationusingvisiontransformer AT kurniawannurramadhani cocoaripenessclassificationusingvisiontransformer |