ArticleData in brief2023
A dataset of pomegranate growth stages for machine learning-based monitoring and analysis.
Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A faster and lighter weight robotic ready model YOLO Punica for detecting pomegranate fruit development.Scientific reports · 2025Article
- Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stages.Data in brief · 2025Article
- YOLO-Granada: a lightweight attentioned Yolo for pomegranates fruit detection.Scientific reports · 2024Article
- An India soyabean dataset for identification and classification of diseases using computer-vision algorithms.Data in brief · 2024Article
- A dataset of the quality of soybean harvested by mechanization for deep-learning-based monitoring and analysis.Data in brief · 2024Article
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Authors and funding
4 authors.
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Abstract
Machine learning and deep learning have grown very rapidly in recent years and are widely used in agriculture. Neat and clean datasets are a major requirement for building accurate and robust machine learning models and minimizing misclassification in real-time environments. To achieve this goal, we created a dataset of images of pomegranate growth stages. These images of pomegranate growth stages were taken from May to September from an orchard inside the Henan Institute of Science and Technology in China. The dataset contains 5857 images of pomegranates at different growth stages, which are labeled and classified into five periods: bud, flower, early-fruit, mid-growth and ripe. The dataset consists of four folders, which respectively store the images, two formats of annotation files, and the record files for the division of training, validation, and test sets. The authors have confirmed the usability of this dataset through previous research. The dataset may help researchers develop computer applications using machine learning and computer vision algorithms.
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