GrapeMOTS: UAV vineyard dataset with MOTS grape bunch annotations recorded from multiple perspectives for enhanced
Mar Ariza-Sentís1, Kaiwen Wang1, Zhen Cao1
1Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, Netherlands.
Data in Brief
|May 3, 2024
Summary
This study addresses undercounting in fruit detection and tracking by using multiple viewing angles from UAVs. It highlights the need for better datasets, especially for grape bunch detection and tracking, to improve agricultural technology.
Area of Science:
- Agricultural Technology
- Computer Vision
- Robotics
Background:
- Object Detection and Tracking (ODT) algorithms automate fruit counting, reducing human labor.
- Manual fruit counting is time-consuming and error-prone, especially with large yields.
- ODT algorithms struggle with undercounting due to fruit occlusion (e.g., hidden by leaves).
Purpose of the Study:
- To address the scarcity of public datasets for grape bunch detection and tracking.
- To improve ODT algorithm accuracy by overcoming occlusion challenges.
- To leverage Unmanned Aerial Vehicle (UAV) imagery for comprehensive data acquisition.
Main Methods:
- Utilizing object detection and tracking algorithms.
- Acquiring image and video data from multiple viewing angles using a UAV.
- Focusing on datasets for grape bunch detection and tracking.
Main Results:
- Identified undercounting as a key challenge for ODT in agriculture.
- Highlighted the necessity of multi-angle data acquisition to mitigate occlusion.
- Pointed out the lack of labeled datasets for specific fruits like grape bunches.
Conclusions:
- Multi-angle UAV data is crucial for improving fruit detection and tracking accuracy.
- Developing comprehensive, labeled datasets is essential for advancing agricultural ODT.
- This research aims to enhance automated fruit counting systems for farmers.
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