A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector
The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the pre...
| Published in: | Bulletin of Electrical Engineering and Informatics |
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2024
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196533315&doi=10.11591%2feei.v13i4.5705&partnerID=40&md5=7c906a050703aa0fed339d91c8d95341 |
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2-s2.0-85196533315 Ani A.I.C.; Farid M.A.H.M.; Kamaruzaman A.S.F.; Ahmad S.; Hadi M.S. A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector 2024 Bulletin of Electrical Engineering and Informatics 13 4 10.11591/eei.v13i4.5705 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196533315&doi=10.11591%2feei.v13i4.5705&partnerID=40&md5=7c906a050703aa0fed339d91c8d95341 The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures. © 2024, Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 20893191 English Article All Open Access; Hybrid Gold Open Access |
| author |
Ani A.I.C.; Farid M.A.H.M.; Kamaruzaman A.S.F.; Ahmad S.; Hadi M.S. |
| spellingShingle |
Ani A.I.C.; Farid M.A.H.M.; Kamaruzaman A.S.F.; Ahmad S.; Hadi M.S. A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector |
| author_facet |
Ani A.I.C.; Farid M.A.H.M.; Kamaruzaman A.S.F.; Ahmad S.; Hadi M.S. |
| author_sort |
Ani A.I.C.; Farid M.A.H.M.; Kamaruzaman A.S.F.; Ahmad S.; Hadi M.S. |
| title |
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector |
| title_short |
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector |
| title_full |
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector |
| title_fullStr |
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector |
| title_full_unstemmed |
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector |
| title_sort |
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector |
| publishDate |
2024 |
| container_title |
Bulletin of Electrical Engineering and Informatics |
| container_volume |
13 |
| container_issue |
4 |
| doi_str_mv |
10.11591/eei.v13i4.5705 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196533315&doi=10.11591%2feei.v13i4.5705&partnerID=40&md5=7c906a050703aa0fed339d91c8d95341 |
| description |
The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures. © 2024, Institute of Advanced Engineering and Science. All rights reserved. |
| publisher |
Institute of Advanced Engineering and Science |
| issn |
20893191 |
| language |
English |
| format |
Article |
| accesstype |
All Open Access; Hybrid Gold Open Access |
| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1809678149847351296 |