Update README.md

README.md

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 ### Dataset Description
 
-The collection was done on multiple farms in Ghana and Uganda.
-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 in Ghana and A total of 6,086 drone images, comprising 3,000 for coffee and 3,086 for cashew, were collected due to these field activities. 
+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
+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
+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.
-Annotation of cashew trees, flowers, immature, mature, ripped and spoilt cashew, cocoa fruits and coffee was done over a period of 2 months.
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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).
 The Dataset was compiled by two teams:
  * KaraAgro AI Foundation (Ghana)
  * Makerere AI Lab (Uganda)
 
 
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 ## Intended uses
 
-You can use the dataset for object detection on cashew, coffee and Cocoa images.
-The dataset was initially developed to inform users on yield estimation of crops:
-- cashew trees
-- flowers
-- immature
-- mature
-- ripped
-- spoilt cashew
-- cocoa fruits
-- coffee
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-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.
+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 along with associated metadata (datasheets, annotation protocols, etc) have been uploaded to DataVerse, an open-source data repository. 
+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. 
 
-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 : <a href = "https://doi.org/10.57967/hf/0959"> doi.org/10.57967/hf/0959 </a>
 
 
-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.
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