DOI: 10.57760/sciencedb.30993
title: Low-altitude UAV Visible Light Remote Sensing Tobacco Identification Dataset for Complex Scene Deconstruction
title: 复杂场景解构的低空无人机可见光遥感烟草识别数据集

description: To address the challenges of low resolution in traditional satellite remote sensing, inefficient ground surveys, and insufficient existing datasets for precision monitoring of tobacco cultivation in complex mountainous regions, this study selected the tobacco growing base in Beipanjiang Town, Zhenfeng County, Qianxinan Prefecture, Guizhou Province as its research area. Data collection was conducted in June 2021 during the tobacco root extension phase. Employing ENVI 5.3 and ArcGIS software, manual visual interpretation was used to delineate plant outlines for sample annotation. Geometric correction, radiometric calibration, and image mosaicking were performed using Pix4Dmapper software, generating orthorectified imagery with a spatial resolution of 6.4 cm. This process established a finely annotated semantic segmentation dataset tailored for tobacco. This dataset comprises five 5000*5000-pixel ultra-low-altitude UAV remote sensing images, segmented into an initial dataset (2300 samples) and an optimised dataset (9500 samples) each at 224*224 pixels, alongside corresponding manually annotated maps in .png and .shp formats. Accuracy assessments reveal: Among eight scenario categories, the highest precision (0.85) was achieved in fragmented terrain without weeds, while the lowest (0.49) occurred in flat plots with weeds. Overall image recognition accuracy without scenario differentiation reached 0.68, demonstrating significantly enhanced precision after deconstructing complex scenarios. This dataset provides crucial data support for deep learning models to accurately identify surface crops in complex mountainous terrain, thereby enhancing tobacco precision monitoring and precision agricultural decision-making.
description: To address the challenges of low resolution in traditional satellite remote sensing, inefficient ground surveys, and insufficient existing datasets for precision monitoring of tobacco cultivation in complex mountainous regions, this study selected the tobacco growing base in Beipanjiang Town, Zhenfeng County, Qianxinan Prefecture, Guizhou Province as its research area. Data collection was conducted in June 2021 during the tobacco root extension phase. Employing ENVI 5.3 and ArcGIS software, manual visual interpretation was used to delineate plant outlines for sample annotation. Geometric correction, radiometric calibration, and image mosaicking were performed using Pix4Dmapper software, generating orthorectified imagery with a spatial resolution of 6.4 cm. This process established a finely annotated semantic segmentation dataset tailored for tobacco. This dataset comprises five 5000*5000-pixel ultra-low-altitude UAV remote sensing images, segmented into an initial dataset (2300 samples) and an optimised dataset (9500 samples) each at 224*224 pixels, alongside corresponding manually annotated maps in .png and .shp formats. Accuracy assessments reveal: Among eight scenario categories, the highest precision (0.85) was achieved in fragmented terrain without weeds, while the lowest (0.49) occurred in flat plots with weeds. Overall image recognition accuracy without scenario differentiation reached 0.68, demonstrating significantly enhanced precision after deconstructing complex scenarios. This dataset provides crucial data support for deep learning models to accurately identify surface crops in complex mountainous terrain, thereby enhancing tobacco precision monitoring and precision agricultural decision-making.