Update README.md

README.md

@@ -20,9 +20,12 @@ This repository contains a comprehensive dataset of cashew, cocoa and coffee ima
 
 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)
 
+## Motivation
+This dataset bridges a gap by offering a comprehensive collection of agricultural images specifically designed to fuel the development and evaluation of yield estimation models. Estimating crop yield accurately is a complex task influenced by numerous factors including weather conditions, soil quality, pest prevalence, and cultivation practices. By offering a diverse range of images capturing different crops, growth stages, and environmental conditions, this dataset empowers researchers, data scientists, and agronomists to develop models that are robust and adaptable to the variability inherent in real-world agricultural scenarios.
+
 
 ### Ghana - KaraAgro AI
-Each image in the Ghana set has a resolution of 16000 by 13000 pixels.
+Each image in the Ghana set has a resolution of 16000 by 13000 pixels. There is a total of 8,784 images and annotations in the Ghana set.
 
 #### Dataset Labels
 
@@ -34,7 +37,7 @@ Cocoa --> ['cocoa-pod-mature-unripe', 'cocoa-tree', 'cocoa-pod-immature', 'cocoa
 #### Number of Images
 ```json
 Cashew --> 4,715 images
-Cocoa --> 4, 069 images
+Cocoa --> 4,069 images
 ```
 
 ### Number of Instances Annotated
@@ -45,7 +48,7 @@ Cocoa --> {'cocoa-pod-mature-unripe': 10786, 'cocoa-tree': 2831, 'cocoa-pod-imma
 ```
 
 ### 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 6,086 drone images, comprising 3,000 for coffee and 3,086 for cashew. Each image in the Uganda set has dimensions of 4,000 by 3,000 pixels.
 
 #### Dataset Labels