MangoImageBD: An extensive mango image dataset for identification and classification of various mango varieties in BangladeshMendeley Data - Md Hasanul Ferdaus; Rizvee Hassan Prito; Masud Ahmed; Mohammad Manzurul Islam; Md Sawkat Ali; Muhammad Ibrahim; Ahmed Abdal Shafi Rasel; Maheen Islam; Taskeed Jabid; Md Atiqur Rahman; Md Mizanur Rahoman (Data in Brief, 2025)
The mango image dataset presented in this article contains clear and detailed images of the fifteen most common and popular mango (Mangifera indica) varieties in Bangladesh: Amrapali, Ashshina Classic, Ashshina Zhinuk, Banana Mango, Bari-4, Bari-11, Fazli Classic, Fazli Shurmai, Gourmoti, Harivanga, Himsagor, Katimon, Langra, Rupali, and Shada. The mango specimens were sourced from various fruit markets across six districts of Bangladesh, namely Rajshahi, Chapai Nawabganj, Satkhira, Panchagarh, Rangpur, and Dhaka, which are famous for popular mango cultivation and availability to ensure a wide geographic representation. To maintain the quality and uniformity of images across the dataset, the images were captured using a high-definition smartphone camera under a standardized and controlled environment.Overall, the full dataset contains a total of 28,515 images, where 5703 images are original (raw) and 5703 images are processed with a blend of both real and virtual backgrounds. The processed images were further augmented resulting in a total of 17,109 augmented images. This is done to enhance their utility for training machine learning and deep learning models, particularly for performing computer vision tasks such as object detection, classification, and segmentation. This augmentation includes transformations such as flipping, rotation, shearing, blurring, variation of brightness and exposure, and introduction of noise to simulate diverse real-world scenarios and improve model robustness.This dataset holds strong reuse potential across computer vision, agriculture, food processing, and biodiversity research. It supports automated mango variety identification, sorting, grading, and quality assessment in precision agriculture. It can also aid in breeding climate-resilient, high-yield mango varieties, enhancing food security and sustainable farming. Additionally, it facilitates studies on phenotypic diversity, genetic correlations, and regional trait comparisons. The dataset can help ensure traceability, authenticity, and quality assurance, improving supply chains and export potential. From a biodiversity standpoint, it contributes to documenting and conserving unique mango varieties.