Exploring End-to-End object detection with transformers versus YOLOv8 for enhanced citrus fruit detection within trees
This paper presents a comparative analysis between two state-of-the-art object detection models, DETR and YOLOv8, focusing on their effectiveness in fruit detection for yield prediction in agriculture. The study begins with data acquisition, utilizing images and corresponding annotations to train an...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Systems and Soft Computing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000322 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850054685198647296 |
|---|---|
| author | Zineb Jrondi Abdellatif Moussaid Moulay Youssef Hadi |
| author_facet | Zineb Jrondi Abdellatif Moussaid Moulay Youssef Hadi |
| author_sort | Zineb Jrondi |
| collection | DOAJ |
| description | This paper presents a comparative analysis between two state-of-the-art object detection models, DETR and YOLOv8, focusing on their effectiveness in fruit detection for yield prediction in agriculture. The study begins with data acquisition, utilizing images and corresponding annotations to train and evaluate the models. Our approach employs a data-driven methodology, dividing the dataset into training and testing sets, with rigorous validation to ensure robustness.For DETR, evaluation results demonstrate promising performance across various IoU thresholds, indicating its effectiveness in accurately localizing fruits within bounding boxes. Additionally, YOLOv8 exhibits substantial improvements in detection performance, achieving high precision and recall rates, particularly noteworthy for ''orange'' and ''sweet_orange'' classes. Notably, the model showcases commendable proficiency even in challenging scenarios.In conclusion, both DETR and YOLOv8 offer valuable insights for precision farming, aiding farmers in yield prediction and harvest planning. While DETR demonstrates robustness and efficiency in fruit detection, YOLOv8 excels in high-precision detection, albeit with longer training times. These findings highlight the potential of advanced object detection models in revolutionizing agricultural practices, contributing to enhanced productivity and market equilibrium. |
| format | Article |
| id | doaj-art-6035c76b9ae240ea8d367aee72d06444 |
| institution | DOAJ |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-6035c76b9ae240ea8d367aee72d064442025-08-20T02:52:10ZengElsevierSystems and Soft Computing2772-94192024-12-01620010310.1016/j.sasc.2024.200103Exploring End-to-End object detection with transformers versus YOLOv8 for enhanced citrus fruit detection within treesZineb Jrondi0Abdellatif Moussaid1Moulay Youssef Hadi2Laboratory for Computer Science Research, Faculty of Sciences Ibn Tofail University, Kénitra, MoroccoENSIAS, Mohammed V University in Rabat, Rabat, 10000, Morocco; Corresponding author.Laboratory for Computer Science Research, Faculty of Sciences Ibn Tofail University, Kénitra, MoroccoThis paper presents a comparative analysis between two state-of-the-art object detection models, DETR and YOLOv8, focusing on their effectiveness in fruit detection for yield prediction in agriculture. The study begins with data acquisition, utilizing images and corresponding annotations to train and evaluate the models. Our approach employs a data-driven methodology, dividing the dataset into training and testing sets, with rigorous validation to ensure robustness.For DETR, evaluation results demonstrate promising performance across various IoU thresholds, indicating its effectiveness in accurately localizing fruits within bounding boxes. Additionally, YOLOv8 exhibits substantial improvements in detection performance, achieving high precision and recall rates, particularly noteworthy for ''orange'' and ''sweet_orange'' classes. Notably, the model showcases commendable proficiency even in challenging scenarios.In conclusion, both DETR and YOLOv8 offer valuable insights for precision farming, aiding farmers in yield prediction and harvest planning. While DETR demonstrates robustness and efficiency in fruit detection, YOLOv8 excels in high-precision detection, albeit with longer training times. These findings highlight the potential of advanced object detection models in revolutionizing agricultural practices, contributing to enhanced productivity and market equilibrium.http://www.sciencedirect.com/science/article/pii/S2772941924000322Fruit detectionObject detectionDETRYOLOv8Yield prediction |
| spellingShingle | Zineb Jrondi Abdellatif Moussaid Moulay Youssef Hadi Exploring End-to-End object detection with transformers versus YOLOv8 for enhanced citrus fruit detection within trees Systems and Soft Computing Fruit detection Object detection DETR YOLOv8 Yield prediction |
| title | Exploring End-to-End object detection with transformers versus YOLOv8 for enhanced citrus fruit detection within trees |
| title_full | Exploring End-to-End object detection with transformers versus YOLOv8 for enhanced citrus fruit detection within trees |
| title_fullStr | Exploring End-to-End object detection with transformers versus YOLOv8 for enhanced citrus fruit detection within trees |
| title_full_unstemmed | Exploring End-to-End object detection with transformers versus YOLOv8 for enhanced citrus fruit detection within trees |
| title_short | Exploring End-to-End object detection with transformers versus YOLOv8 for enhanced citrus fruit detection within trees |
| title_sort | exploring end to end object detection with transformers versus yolov8 for enhanced citrus fruit detection within trees |
| topic | Fruit detection Object detection DETR YOLOv8 Yield prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000322 |
| work_keys_str_mv | AT zinebjrondi exploringendtoendobjectdetectionwithtransformersversusyolov8forenhancedcitrusfruitdetectionwithintrees AT abdellatifmoussaid exploringendtoendobjectdetectionwithtransformersversusyolov8forenhancedcitrusfruitdetectionwithintrees AT moulayyoussefhadi exploringendtoendobjectdetectionwithtransformersversusyolov8forenhancedcitrusfruitdetectionwithintrees |