Pomegranate Images Dataset

Published: 25 July 2023| Version 5 | DOI: 10.17632/kgwsthf2w6.5
Jifei Zhao, Rolla Almodfer


The dataset consists of images capturing various growth stages of pomegranates. The images were collected from May to September in an orchard located within the Henan Institute of Science and Technology in China. There are a total of 5857 images in the dataset, and each image is labeled and classified into one of five periods: bud, flower, early-fruit, mid-growth, and ripe. The dataset was curated and hosted by the School of Computer Science and Technology at Henan University of Science and Technology, in collaboration with researchers from the Artificial Light Plant Factory at the same university. The purpose of creating this dataset is to support the development of computer applications that utilize machine learning and computer vision algorithms. By studying the different growth stages of pomegranates, researchers can effectively monitor the condition of each growth stage and enable early detection of any abnormal growth conditions. This early detection can be beneficial for farmers, as it can help them minimize economic losses.



Agricultural Engineering, Feature Extraction, Image Database, Image Classification