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---
license: cc-by-4.0
datasets:
- KaraAgroAI/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation
language:
- en
library_name: yolo
tags:
- object detection
- vision
- yolo
pipeline_tag: object-detection
metrics:
- mape
---
## Drone-based Agricultural Dataset for Crop Yield Estimation
### Dataset Description
Images of cashew, cocoa and coffee were collected from Uganda and Ghana using drones. Each high-resolution image is accompanied by meticulously annotated labels.
#### Ghana
4,715 instances of cashew images and 4,069 instances of cocoa images. Each image in the Ghana set has a resolution of 16000 by 13000 pixels. See data sheet for more information.
#### Uganda
A total of 6,086 drone images, comprising 3,000 for coffee and 3,086 for cashew. See data sheet for more information on the dataset.
A total of 3000 coffee yield data points were collected in Uganda.
The Drone-based Agricultural Dataset for Crop Yield Estimation via [HuggingFace](https://huggingface.co/datasets/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation).
The Dataset was compiled by two teams:
* KaraAgro AI Foundation (Ghana)
* Makerere AI Lab (Uganda)
## Intended uses
The dataset was mainly developed for yield estimation. The dataset could be used for further research including crop abnormality detection.
Our datasets comply with the Findable, Accessible, InterOperable, and Reusable (FAIR) data principles.
The datasets have been assigned a Digital Object Identifier (DOI), a permanent unique identifier to facilitate findability and accessibility. The metadata is citable and includes domain-specific and file-level data that map to metadata standards within machine learning, computer vision, data analysis - geospatial and time series analysis to make it Interoperable.
Our datasets along with their associated metadata may be accessed and downloaded via this link : <a href = "https://doi.org/10.57967/hf/0959"> doi.org/10.57967/hf/0959 </a>
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