Pomegranate Fruit Diseases Dataset for Deep Learning Models

Published: 15 November 2023| Version 1 | DOI: 10.17632/b6s2rkpmvh.1


Pomegranate Fruit Diseases datasets should be efficient and clean for creating accurate and consistent machine-learning models and minimizing misclassification in real-time scenarios. The publicly available, standardized datasets for the categorization of pomegranate fruit illnesses currently used in agriculture are insufficiently precise and comprehensive to effectively train the models. Our main objective for the current project is to produce a publicly accessible dataset of pomegranate fruits and an image dataset of pomegranate fruits with various diseases that is ready for use to address this issue. Five different pomegranate fruit diseases—from areas like Ballari, Bangalore, Bagalkote, etc.—have been compiled by us. The photos were shot in July and October of 2023. The collection includes 5099 labeled and categorized images of healthy and diseased pomegranate fruit, divided into five categories: Healthy, Bacterial blight, Anthracnose, Cercospora fruit spot, and Alternaria fruit spot. The dataset consists of five folders named by related medical conditions. This dataset may help identify pomegranate diseases in other countries and boosting pomegranate yield output.



Presidency University, Visvesvaraya Technological University, RV College of Engineering


Agricultural Science, Artificial Intelligence, Computer Vision, Image Processing, Disease, Machine Learning, Fruit, Image Classification, Classification System, Pomegranate, Deep Learning