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--- |
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license: cc-by-4.0 |
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datasets: |
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- KaraAgroAI/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation |
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language: |
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- en |
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library_name: yolo |
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tags: |
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- object detection |
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- vision |
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- yolo |
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pipeline_tag: object-detection |
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metrics: |
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- mape |
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--- |
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## Drone-based Agricultural Dataset for Crop Yield Estimation |
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### Dataset Description |
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Images of cashew, cocoa and coffee were collected from Uganda and Ghana using drones. Each high-resolution image is accompanied by meticulously annotated labels. |
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#### Ghana |
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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. |
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#### Uganda |
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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. |
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A total of 3000 coffee yield data points were collected in Uganda. |
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The Drone-based Agricultural Dataset for Crop Yield Estimation via [HuggingFace](https://huggingface.co/datasets/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation). |
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The Dataset was compiled by two teams: |
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* KaraAgro AI Foundation (Ghana) |
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* Makerere AI Lab (Uganda) |
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## Intended uses |
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The dataset was mainly developed for yield estimation. The dataset could be used for further research including crop abnormality detection. |
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Our datasets comply with the Findable, Accessible, InterOperable, and Reusable (FAIR) data principles. |
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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. |
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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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