MangoBD: An Extensive Image Dataset of Common Mango Varieties in Bangladesh for Identification and Classification
Description
Type of data: 504x1120 px mango images. Data format: JPEG. Contents of the dataset: Common varieties of mangoes in Bangladesh. Number of classes: Fifteen (15) common varieties of mangoes of Bangladesh - (1) Amrapali, (2) Ashshina Classic, (3) Ashshina Zhinuk, (4) Banana Mango, (5) Bari-4, (6) Bari-11, (7) Fazli Classic, (8) Fazli Shurmai, (9) Gourmoti, (10) Harivanga, (11) Himsagor, (12) Katimon, (13) Langra, (14) Rupali, and (15) Shada. Number of images: Total number of images in the dataset: 28,509. (1) Total original (raw) images of mango cultivars (MangoOriginal) = 5,703, (2) Total processed images with a blend of both real and virtual backgrounds (MangoRealVirtual) = 5,703, and (3) Total augmented images (MangoAugmented)= 17,103. Distribution of instances: (1) Original (raw) images in each class of the mango cultivars (MangoOriginal): Amrapali = 135, Ashshina Classic = 571, Ashshina Zhinuk = 1,286, Banana Mango = 83, Bari-4 = 74, Bari-11 = 1,244, Fazli Classic = 171, Fazli Shurmai = 247, Gourmoti = 630, Harivanga = 265, Himsagor = 106, Katimon = 424, Langra = 120, Rupali = 184, and Shada = 163. (2) Processed images with a blend of both real and virtual backgrounds (MangoRealVirtual): Amrapali = 135, Ashshina Classic = 571, Ashshina Zhinuk = 1,286, Banana Mango = 83, Bari-4 = 74, Bari-11 = 1,244, Fazli Classic = 171, Fazli Shurmai = 247, Gourmoti = 630, Harivanga = 265, Himsagor = 106, Katimon = 424, Langra = 120, Rupali = 184, and Shada = 163. (3) Augmented images for each class of the mango cultivars (MangoAugmented): Amrapali = 405, Ashshina Classic = 1,713, Ashshina Zhinuk = 3,848, Banana Mango = 249, Bari-4 = 222, Bari-11 = 3,726, Fazli Classic = 513, Fazli Shurmai = 741, Gourmoti = 1,890, Harivanga = 795, Himsagor = 318, Katimon = 1,272, Langra = 360, Rupali = 552, and Shada = 489. Dataset size: Total size of the dataset = 1.35 GB and the ZIP compressed size = 1.16 GB. Data acquisition process: Images of various mango varieties are captured through high-definition smartphone cameras focusing from different angles. Data source location: Local wholesale and retail fruit markets located in different areas of Dhaka and Rangpur districts in Bangladesh. Where applicable: Training and evaluating machine learning and deep learning models to identify and classify mango varieties in Bangladesh which can be useful in smart horticulture, precision farming, supply chain automation, ecology and ecosystem health monitoring, and biodiversity and conservation efforts.