Update README.md

README.md

@@ -16,33 +16,64 @@ metrics:
 
 ## Drone-based Agricultural Dataset for Crop Yield Estimation
 
-### Dataset Description
+This repository contains a comprehensive dataset of cashew, cocoa and coffee images captured by drones, accompanied by meticulously annotated labels. To facilitate 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.
 
-Images of cashew, cocoa and coffee were collected from Uganda and Ghana using drones. Each high-resolution image is accompanied by meticulously annotated labels.
+The dataset was collected by teams from Ghana (KaraAgro AI) and Uganda (Makerere AI Lab, Uganda Marconi Lab, National Coffee Research Institute, National Crops Resources Research Institute)
 
-#### 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
+### Ghana - KaraAgro AI
+Each image in the Ghana set has a resolution of 16000 by 13000 pixels.
+
+#### Dataset Labels
+
+```
+Cashew --> ['cashew_tree', 'flower', 'immature', 'mature', 'ripe', 'spoilt']
+Cocoa --> ['flower', 'immature', 'mature', 'ripe', 'spoilt', 'tree']
+```
+
+#### Number of Images
+```json
+Cashew --> 4,715 images
+Cocoa --> 4, 069 images
+```
+
+### 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.
+#### Dataset Labels
+
+```
+Cashew --> ['cashew_tree', 'flower', 'immature', 'mature', 'ripe', 'spoilt']
+Coffee --> ['unripe', 'ripening', 'ripe', 'spoilt', 'coffee']
+```
+
+#### Number of Images
+```json
+Cashew --> 3,086 images
+Coffee --> 3,000 images
+```
 
-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)
+### Folder structure
 
+```markdown
+Data/
+└── Ghana/
+    ├── cashew.zip
+    ├── cocoa.zip
+└── Uganda/
+    ├── cashew.zip
+    ├── coffee.zip
+```
 
-## Intended uses
+### Intended uses
 
-The dataset was mainly developed for yield estimation. The dataset could be used for further research including crop abnormality detection.
+The dataset which was mainly developed for yield estimation can also be usedfor further research including crop abnormality detection due to the presence of spoilt classes in the datasets
 
 
-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. 
+### Dataset Information
+The dataset was created by a team of data scientists from the KaraAgro AI Foundation, with support from the agricultural scientists and officers. The creation of this dataset was made possible through the funding from the Lacuna Fund. For detailed information regarding the datasheet, we invite you to explore the accompanying datasheet available [here](https://). This comprehensive resource offers a deeper understanding of the dataset's compostion, variables, data collection methodologies, and othe relevant details.
 
-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>
+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>