Data · dataset · 2026
Low-altitude UAV Visible Light Remote Sensing Tobacco Identification Dataset for Complex Scenarios
Listed in ScienceDB
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.
Description
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.
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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.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.30993 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Geomatic engineering 75% · Image 75% · Satellite remote sensing 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.30993 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4013 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (75%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:remote-sensing | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |