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 are important to the establishment and survival as well as foundation for lifelong growth. 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 5364 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.