Published: 28 December 2022| Version 1 | DOI: 10.17632/z6dtfdpzz8.1
Nashwa El-Bendary, Esraa Elhariri


- An annotated benchmark image dataset for training and validation of strawberry ripeness detection systems based on Machine learning (ML) and, Deep Learning (DL). - 247 Raw RGB digital images (.jpg) of strawberry fruits were taken in an orchard of the Central Laboratory for Agricultural Climate (CLAC), Agricultural Research Center, Cairo - Egypt. -The images have been captured from the fruit top view considering different view angles using Sony Xperia Z2 LTE-A D6503 smartphone 20.7 MP camera with a CMOS sensor system and resolution of 3840 x 2160 pixels (Mpix). The dataset images, which contain both fully-visible strawberry fruits and partially-visible strawberry fruits concealed by leaves or by other fruits, were manually annotated, using Roboflow Annotate annotation tool. The data formats of files in Strawberry-DS dataset are RGB digital images (.jpg) and their corresponding YOLO format (.txt) annotation files.



Object Detection, Machine Learning, Fruit, Developmental Stages, Food Ripening, Strawberry, Deep Learning, Crop Post Harvest Technology