RiGaD: An aerial dataset of rice seedlings for assessing germination rates and density

Hieu Luu Trong, Hoang-Long Cao, Quang Hieu Ngo, Thanh Tam Nguyen, Ilias El Makrini, Bram Vanderborght

Research output: Contribution to journalArticlepeer-review

Abstract

The popularity of Unmanned Aerial Vehicles (UAVs) in agriculture makes data collection more affordable, facilitating the development of solutions to improve agricultural quality. We present a dataset of rice seedlings extracted from aerial images captured by a UAV under various environmental conditions. We focus on rice seedlings cultivated by the sowing method during their early growth stages because these stages set the foundation for the plant's entire life cycle and ultimately determine the yield. We employed an adaptive thresholding method to isolate rice seedlings from the aerial images. We subsequently classified them into three categories based on their germination conditions: single rice seedings, clustered rice seed plants, and undefined objects. We obtained a total of 5,364 labeled images of rice seedlings through data augmentation. This dataset serves as a resource for assessing germination rates and density using machine learning methods. The results derived from these assessments help farmers understand seedling growth and enable them to monitor the health and vigor of rice seedling during early growth stages.
Original languageEnglish
Article number111118
Pages (from-to)1-8
Number of pages8
JournalData in Brief
Volume57
DOIs
Publication statusPublished - Dec 2024

Bibliographical note

Funding Information:
The authors acknowledge that this work was supported by the Brussels Institute of Advanced Studies (Grant: BrIAS2024) and by a scientific stay grant from the FWO (Grant number: V501724N).

Publisher Copyright:
© 2024 The Authors

Keywords

  • Rice; seedlings; sowing; UAV; image processing; dataset; machine learning

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