Data · dataset · 2023
Tobacco Aerial Dataset
Listed in Teesside University Research Data Repository
To capture aerial dataset of tobacco and weed, a Mavic Mini drone was employed.
Description
Between the ages of 15 and 40 days, eight tobacco fields in Mardan, Khyber Pakhtunkhwa, Pakistan, were photographed. The dataset is captured at an average altitude of 4 metres and a ground sampling distance of 0.1 cm per pixel.
Pictures were captured for both datasets at a resolution of 1920 × 1080 pixels, however for faster processing, we cropped non-overlapping images to a size of 480 x 352 pixels. Using the MATLAB Image Labeler app, we labelled the photos by hand. A label image in 8-bit unsigned grayscale is produced by the software.
Read the rest (5 more)
The depicted image has the pixel values 0, 1, and 2 assigned to the background, crop, and weed, respectively. Since there are no aerial dataset available publicly to the best of our knowledge, these datasets could be utilized to support further research for Tobacco crop. Tobacco dataset is captured in eight different drone fly campaigns on eight different fields as shown in table below.
Campaign No. Number of images Timing Around/ Date Captured Soil Condition 1 864 2:30pm/09April 21 After irrigation 2 936 3:30pm/07April 21 Before irrigation 3 120 3:38pm/07April 21 After irrigation 4 120 5:52pm/07April 21 Before irrigation 5 120 6:33pm/07April 21 After irrigation 6 120 3:27pm/09April 21 After irrigation 7 120 3:43pm/09April 21 Before irrigation 8 120 3:59pm/09April 21 After irrigation
Citation
Request: if you use these datasets in your research or projects by any means, please cite following publications. 1) Patch-wise weeds coarse segmentation mask from aerial imagery of sesame crop (Published in Computers and Electronics in Agriculture 2022, HEC Recognized W category, Impact factor 6.757, Q1) 2) Towards automated weed detection through two-stage semantic segmentation of tobacco and weed pixels in aerial Imagery (Published in Smart Agricultural Technology (A companion journal of Computers and Electronics in Agriculture)) 3) A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop (Published in IEEE Access, Impact factor 3.1, Q1) Acknowledgement Request This work is funded by the Higher Education Commission of Pakistan and the National center for Robotics and Automation (DF-1009–31).
Please Acknowledge. Find More data and research papers in related links attached.
Links
Where it is published
- DOI doi.org/10.17632/5dpc5gbgpz.2 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Earth & Environmental Science
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/5dpc5gbgpz.2 | 15 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:tabular | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |