AriAplBud: An Aerial Multi-Growth Stage Apple Flower Bud Dataset for Agricultural Object Detection Benchmarking

Wenan Yuan ()
Additional contact information
Wenan Yuan: Independent Researcher, Oak Brook, IL 60523, USA

Data, 2024, vol. 9, issue 2, 1-16

Abstract: As one of the most important topics in contemporary computer vision research, object detection has received wide attention from the precision agriculture community for diverse applications. While state-of-the-art object detection frameworks are usually evaluated against large-scale public datasets containing mostly non-agricultural objects, a specialized dataset that reflects unique properties of plants would aid researchers in investigating the utility of newly developed object detectors within agricultural contexts. This article presents AriAplBud: a close-up apple flower bud image dataset created using an unmanned aerial vehicle (UAV)-based red–green–blue (RGB) camera. AriAplBud contains 3600 images of apple flower buds at six growth stages, with 110,467 manual bounding box annotations as positive samples and 2520 additional empty orchard images containing no apple flower bud as negative samples. AriAplBud can be directly deployed for developing object detection models that accept Darknet annotation format without additional preprocessing steps, serving as a potential benchmark for future agricultural object detection research. A demonstration of developing YOLOv8-based apple flower bud detectors is also presented in this article.

Keywords: classification; CNN; deep learning; drone; open-source; orchard; precision agriculture; RGB; UAV; YOLOv8 (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/9/2/36/pdf (application/pdf)
https://www.mdpi.com/2306-5729/9/2/36/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:9:y:2024:i:2:p:36-:d:1337423

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().