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Agricultural Task Image Segmentation Data
Data Science and Analytics
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About
Agricultural Image Segmentation provides crucial data for object detection in cultivated areas. This resource focuses on the segmentation of photos taken from drones, detailing various plantations that include crops such as cabbage and zucchini. It addresses modern agricultural challenges by supporting tasks like plant detection, automated counting, health assessment, and strategic irrigation planning. The data is essential for developing sophisticated agricultural machine learning models.
Columns
The supporting data file, plantations.csv, features two key fields derived from the image annotation process:
- image_id: This field represents the identifier taken directly from the associated XML file.
- image_name: This field specifies the directory path where the image is located, offering a unique value for each of the thirteen images recorded.
Distribution
The core data structure includes original plantation images in an
img folder, accompanied by XML annotations. The format is designed to support detailed object detection, with XML files indicating the coordinates of polygons for segmentation, providing both x and y coordinates for each point. The data structure is organized into three main components: original images and XML annotations (Plantations_Segmentation), Object Segmentation masks, and Class Segmentation masks. There are thirteen total values recorded for the image identifiers.The dataset features two primary types of segmentation: Object Segmentation, where all individual objects are identified separately, and Class Segmentation, where objects belonging to the same class are grouped together.
Usage
Ideal applications for this dataset include training models for semantic segmentation and object recognition in agricultural fields. It can be used for building systems that automate yield estimation, perform precise plant recognition, and monitor crop stress or disease from aerial imagery. Researchers can also utilize it to develop and test new algorithms for processing land cover data.
Coverage
This data set provides aerial imagery covering agricultural settings, specifically featuring crops like zucchini and cabbage. The focus is geographic segmentation relevant to object detection within plantations. There are no expected updates to this resource.
License
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Who Can Use It
- Data Scientists: For training object detection models specifically targeting plants and crops.
- Technology Developers: Creating precision agriculture tools for farmers and agribusinesses.
- Academic Researchers: Analysing remote sensing data quality and developing plant recognition algorithms.
Dataset Name Suggestions
- Aerial Plantation Object Detection Dataset
- Drone Imagery for Crop Segmentation
- Agricultural Task Image Segmentation Data
- Plant Health Assessment Imagery
Attributes
Original Data Source: Agricultural Task Image Segmentation Data
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