README.md

---
license: cc-by-4.0
language:
- en
pretty_name: Sunflower density estimation dataset from April to July 2024
size_categories:
- 100K<n<1M
---
# Dataset Metadata

## Identification Information

### Citation

- **Title**: Sunflower density estimation dataset from April to July 2024  
- **Originator**: Sofia University - Faculty of Mathematics and Informatics, SAP LABS Bulgaria 
- **Publication Date**: 2024.11.12

### Abstract

Determining plant density in the early stages of crop development is crucial for planning future farming activities. 
This metric is essential for assessing germination rates, forecasting yields, and mapping a field’s growth potential. 
Traditional methods involve manually counting plants in specific areas and extrapolating the data to the entire field.
Modern techniques utilize data from aerial observation platforms, such as satellites and UAVs.
In this study, DJI P4 Multispectral - one of the leading, integrated UAV platforms, was used to collect a comprehensive dataset, 
tailored to sunflower plant density estimation. 
This dataset includes both aerial orthophotos and detailed low-altitude images taken from various heights, that cover the 
active vegetation period of the plants.
 

### Purpose

This dataset was developed as part of a research project, investigating the capabilities and application of drones and multispectral cameras for the agricultural domain.
The provided data can be used for the following scenarios:
1) Training models, relying on multispectral data sources.
2) Improving existing algorithms in the computer vision domain.
3) Developing and validating methods for sunflower density estimation.

## Time Period of Content

- **Single Date/Time**: Start Date 2024-04-15 to End Date 2024-07-24

## Data Quality Information

Composite images (orthophotos) have been generated with DJI Terra, with 75% frontal and 60% side overlap.
Some of the surveys have been completed in suboptimal weather conditions (partly cloudy). This resulted in visible variation in color and reflectances 
in several regions of the orthophotos.
Although there was an effort to have surveys executed at the same time of day (around noon), there were cases when we arrived later at the field.
The raw data is validated to be complete - representing the entirety of the observed field for every survey. An accompanying validation script is provided with 
the dataset.

### Horizontal Coordinate System

- **Geographic Coordinate System**: EPSG:4326
  - **Angular Unit**: Decimal degrees
  - **Datum**: WGS 84
  - **Prime Meridian**: Greenwich
  - **Domain**: Raster

## Entity and Attribute Information

### Detailed Description

#### Entities
Data is organized into directories. Each directory corresponds to one survey and uses **DD.MM.YYYY** format.

Each survey directory contains the following subdirectories:
- **aerial** - raw aerial footage, used during the reconstruction of the orthophoto with DJI Terra.
- **terra** - resulting orthophotos. There are two subdirectories, `default/map` and `lu/map`. The former is a reconstruction with default settings, whereas in the latter, the light uniformity switch was activated.
  - There is a `result.tif` file, corresponding to the RGB orthophoto and 5 orthophotos for each band, following the `result_<Blue, Green, NIR, Red, RedEdge>.tif` naming pattern.
  - There are two subdirectories with 5 vegetation index orthophotos, calculated by DJI Terra (GNDVI, LCI, NDRE, NDVI, OSAVI).
    - **index_map** - these orthophotos contain the vegetation index values in `float32` (range is -1:1)
    - **index_map_color** - these orthophotos contain a "false color" render of the vegetation index values, for the purposes of visualization.
  - In addition, there are .prj projection file and .tfw georeference file for each orthophoto.
  - **report** - this directory contains some metadata, generated during the reconstruction process. For example, `overlap_render.png` illustrates the stitching process.
- **XXm** - where `XX` is either 2, 5, 10 or 40, contains the low-altitude images. For each of the 32 surveying points, there is one RGB image in JPEG and 5 images in TIFF format (corresponding to the 5 bands),
   

All images are geo-referenced, and contain timestamps, image quality, camera properties and other metadata.


#### Capture aperture

Drone surveys are executed with DJI Phantom 4 Multispectral drone. The drone uses the following sensors to capture data:

Sensors: Six 1/2.9” CMOS 

Filters: 

 - Blue (B): 450 nm ± 16 nm
 - Green (G): 560 nm ± 16 nm
 - Red (R): 650 nm ± 16 nm
 - Red edge (RE): 730 nm ± 16 nm
 - Near-infrared (NIR): 840 nm ± 26 nm

Lenses: 

- FOV (Field of View): 62.7°
- Focal Length: 5.74 mm
- Aperture: f/2.2

Software used for generating composite images: DJI Terra Agriculture 4.2.5. 

## Metadata Reference Information

- **Metadata Contact**:
  - **Name**: Pavel Genevski
  - **Organization**: SAP LABS Bulgaria
  - **Position**: Research expert
  - **Email**: pavel.genevski@sap.com

- **Metadata Date**: Date of creating this metadata (2024.11.12)
- **Metadata Standard Name**: FGDC Content Standard for Digital Geospatial Metadata

## Additional Information

- **Keywords**: agriculture, multispectral, crop, sunflower
- **Access Constraints**: CC BY 4.0
- **Use Constraints**: CC BY 4.0