---
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

The collection was done on multiple farms.
Collected image data of cashew and cocoa crops using a DJI P4 Multispectral Drone
Collected 4,715 instances of cashew images and 4,069 instances of cocoa images
Annotation of cashew trees, flowers, immature, mature, ripped and spoilt cashew and cocoa fruits was done over a period of 2 months.
The Drone-based Agricultural Dataset for Crop Yield Estimation via[HuggingFace](https://huggingface.co/datasets/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation).
This involved three annotators with supervision from an agricultural scientist.
Before annotation, an annotation protocol was designed by the agricultural scientist for annotators to follow. The tool used for the annotation was Makesense.ai
Recorded quantitative measures of progress, including the number of observations and recordings collected. Every important detail relating to the data collection has been recorded and made available.


## Intended uses

You can use the dataset for object detection on cashew images.

The dataset was initially developed to inform users to detect:
- cashew trees
- flowers
- immature
- mature,
- ripped
- spoilt cashew
- cocoa fruits 

The dataset could be used for further research including crop abnormality detection. The machine learning data community is a potential user of the dataset.
Updates to the dataset will be communicated to the public through the datasheet or data cards on data hosting websites.
The dataset and the datasheet will be made publicly available. Any contribution can be directed to the authors, KaraAgro AI and Makerere University.

Our datasets comply with the Findable, Accessible, InterOperable, and Reusable (FAIR) data principles. The datasets along with associated metadata (datasheets, annotation protocols, etc) have been uploaded to DataVerse, an open-source data repository. The datasets have been assigned a Digital Object Identifier (DOI), a permanent unique identifier to facilitate findability and accessibility. In addition, the dataset have been mirrored to local servers as a form of backup. 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. The metadata has been published and made available to provide a description of the datasets, data acquisition, preprocessing, and annotation procedures, envisaged use cases for our dataset, and any other information that supports understanding the context and composition of the data and ensure that they are reusable. 
Our datasets along with their associated metadata may be accessed and downloaded via this link : doi:10.57967/hf/0941
Our dataset has been published under the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0). 
This licence gives anyone permission to use, copy, edit, transform and redistribute the dataset as they wish for any purpose, including use for commercial purposes. However, the user of this dataset is required to give appropriate credit by citing us as the source of the original dataset. An appropriate method for citation of this dataset has also been provided.