Strawberry-DS

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

Description

- 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.

Files

Categories

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

License