2026-10-11T17:34:11Z https://www.scidb.cn/oai

10.57760/sciencedb.30993 2026-03-30T13:56:45Z

2026-03-30 Low-altitude UAV Visible Light Remote Sensing Tobacco Identification Dataset for Complex Scenarios doi:10.57760/sciencedb.30993 en To address the challenges of low resolution in traditional satellite remote sensing, inefficient ground surveys, and insufficient existing datasets for precision monitoring of tobacco in complex mountainous regions, this paper employs manual visual interpretation to delineate plant outlines for sample annotation. Following geometric correction, radiometric calibration, and image mosaicking, orthorectified imagery with a spatial resolution of 6.4 cm is generated, thereby constructing a precisely annotated semantic segmentation dataset tailored for tobacco. The dataset comprises five 5000×5000-pixel UAV remote sensing images, randomly segmented into an initial dataset (2300 samples) and an optimised dataset (9500 samples), each consisting of 224×224-pixel segments, alongside corresponding manually annotated labels in PNG format. Results indicate: Among eight scenario types, the highest accuracy (0.85 precision) was achieved in fragmented terrain without weeds, while the lowest accuracy (0.49 precision) occurred in flat plots with weeds. Whole-image recognition accuracy without scenario differentiation was 0.68, with significantly higher accuracy achieved after deconstructing complex scenarios. This dataset provides crucial data and methodological support for deep learning models to accurately identify surface crops in complex mountainous terrain, thereby enhancing precision agricultural decision-making. complex scenarios; UAV visible light remote sensing; tobacco identification; semantic segmentation; precision agriculture yan li hui Shu Guoxiang Lan Yan Huang Youyan Li Qianxia Huang Denghong PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank