Update README.md

README.md

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 ### Dataset Description
 
-The collection was done on multiple farms.
+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
+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. 
+A total of 3000 coffee yield data points were collected in Uganda.
 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.
+The Dataset was compiled by two teams:
+ * KaraAgro AI Foundation (Ghana)
+ * Makerere AI Lab (Uganda)
+
+
+
 
 
 ## Intended uses
 
-You can use the dataset for object detection on cashew images.
-The dataset was initially developed to inform users to detect:
+You can use the dataset for object detection on cashew images and Cocoa images.
+The dataset was initially developed to inform users on yield estimation of the crops:
 - cashew trees
 - flowers
 - immature
 - mature,
 - ripped
 - spoilt cashew
-- cocoa fruits 
+- cocoa fruits
+- coffee
 
 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.