---
license: cc-by-sa-4.0
task_categories:
- object-detection
language:
- en
tags:
- object detection
- vision
size_categories:
- 1K<n<10K

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---

---
## Cashew Disease Identication with Artificial Intelligence (CADI-AI) Dataset

This repository contains a comprehensive dataset of cashew images captured by drones, accompanied by meticulously annotated labels. 
Each high-resolution image in the dataset has a resolution of 1600x1300 pixels, providing fine details for analysis and model training.
To facilitate efficient object detection, each image is paired with a corresponding text file in YOLO format. 
The YOLO format file contains annotations, including class labels and bounding box coordinates.


### Dataset Labels

```
['abiotic', 'insect', 'disease']
```

### Number of Images

```json
{'train': 3788, 'valid': 710, 'test': 238}
```

### Number of Instances Annotated

```json
{'insect':1618, 'abiotic':13960, 'disease':7032}
```
### Folder structure after unzipping repective folders

```markdown
Data/
└── train/
    ├── images
    ├── labels
└── val/
    ├── images
    ├── labels   
└── test/
    ├── images
    ├── labels   
```

### Dataset Information
The dataset was created by a team of data scientists from the KaraAgro AI Foundation, 
with support from agricultural scientists and officers. 
The creation of this dataset was made possible through funding of the 
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) through their projects 
[Market-Oriented Value Chains for Jobs & Growth in the ECOWAS Region (MOVE)](https://www.giz.de/en/worldwide/108524.html) and 
[FAIR Forward - Artificial Intelligence for All](https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/), which GIZ implements on 
behalf the German Federal Ministry for Economic Cooperation and Development (BMZ). 

For detailed information regarding the dataset, we invite you to explore the accompanying datasheet available [here](https://drive.google.com/file/d/1viv-PtZC_j9S_K1mPl4R1lFRKxoFlR_M/view?usp=sharing). 
This comprehensive resource offers a deeper understanding of the dataset's composition, variables, data collection methodologies, and other relevant details.