BananaImageBD: A Comprehensive Image Dataset of Common Banana Varieties with Different Ripeness Stages in Bangladesh.

Type of Data: 256x256 px Banana images.
Data Format: JPEG
Contents of the Dataset: Banana varieties and ripeness stages.

Number of Classes: (1) Four most popular banana varieties in Bangladesh - Bangla Kola, Chompa Kola, Sabri Kola, and Sagor Kola, and (2) Four ripeness stages - Green, Semi-ripe, Ripe, and Overripe.

Number of Images: (1) Total Original (Raw) images of banana varieties = 2,471, Augmented to 7,413 images, and (2) Total Original (Raw) images of ripeness stages = 820, Augmented to 2,457 images.

Distribution of Instances: (1) Original (Raw) images in each class of banana varieties: Bangla Kola = 444, Champa Kola = 994, Sabri Kola = 509, and Sagor Kola = 524; (2) Augmented images in each class of banana varieties: Bangla Kola = 1332, Champa Kola = 2,982, Sabri Kola = 1,527, Sagor Kola = 1,572; (3) Original (Raw) images in each class of Ripeness stages: Green = 212, Semi-ripe = 204, Ripe = 201, and Overripe = 203; (4) Augmented images in each class of Ripeness stages: Green = 636, Semi-ripe = 609, Ripe = 603, and Overripe = 609.

Dataset Size: (1) Total size of the Original (Raw) banana varieties dataset = 17.2 MB; (2) Total size of the Augmented banana varieties dataset = 78.5 MB; (3) Total size of the Original (Raw) ripeness stages dataset = 5.55 MB; and (4) Total size of the Augmented ripeness stages dataset = 25.2 MB.

Data Acquisition Process: Images of bananas are captured using high-quality smartphone cameras.
Data Source Location: Local banana wholesale markets and retail fruit shops located in different places in Bangladesh.
Where Applicable: The dataset presents considerable potential for fostering innovation and developing automated, efficient processes across various industries, such as precision agriculture, food processing, and supply chain management. By training Machine Learning (ML) and Deep Learning (DL) models on this dataset, it becomes possible to accurately classify banana varieties and evaluate their ripeness stages. These models can be utilized to design automated systems for determining ideal harvest times, establishing banana quality control standards, analyzing consumer preferences to guide product development and marketing strategies, and streamlining the supply chain through enhanced harvesting, sorting, packaging, and inventory management. Additionally, researchers focused on advancing Computer Vision technologies in food and agricultural sciences will find the dataset valuable for improving precision farming and food processing methods. As a result, the dataset offers substantial potential for automating banana production and processing, cutting labor costs, and boosting overall operational efficiency.

Note: This dataset is an updated version of "BananaImageBD: An Extensive Image Dataset of Common Bangladeshi Banana Varieties with Different Ripeness Levels", DOI: 10.17632/ptfscwtnyz.1

数据类型：256×256像素的香蕉图像。
数据格式：JPEG。
数据集内容：涵盖香蕉品种与成熟度等级。
类别数量：(1) 孟加拉国四大主流香蕉品种：Bangla Kola、Chompa Kola、Sabri Kola以及Sagor Kola；(2) 四个成熟度等级：青熟期（Green）、半熟期（Semi-ripe）、成熟期（Ripe）以及过熟期（Overripe）。
图像数量：(1) 香蕉品种类别的原始（原生）图像总计2471张，经数据增强后扩充至7413张；(2) 成熟度类别的原始（原生）图像总计820张，经数据增强后扩充至2457张。
样本分布：(1) 各香蕉品种类别的原始（原生）图像数量：Bangla Kola为444张、Champa Kola为994张、Sabri Kola为509张以及Sagor Kola为524张；(2) 各香蕉品种类别的增强后图像数量：Bangla Kola为1332张、Champa Kola为2982张、Sabri Kola为1527张以及Sagor Kola为1572张；(3) 各成熟度类别的原始（原生）图像数量：青熟期为212张、半熟期为204张、成熟期为201张以及过熟期为203张；(4) 各成熟度类别的增强后图像数量：青熟期为636张、半熟期为609张、成熟期为603张以及过熟期为609张。
数据集容量：(1) 香蕉品种类别原始（原生）数据集总容量为17.2 MB；(2) 香蕉品种类别增强后数据集总容量为78.5 MB；(3) 成熟度类别原始（原生）数据集总容量为5.55 MB；(4) 成熟度类别增强后数据集总容量为25.2 MB。
数据采集流程：采用高品质智能手机摄像头拍摄香蕉图像。
数据来源地：孟加拉国各地的本地香蕉批发市场与零售水果店。
应用场景：本数据集在多个行业领域具备显著的创新赋能潜力，可助力开发自动化、高效化的业务流程，应用场景涵盖精准农业、食品加工以及供应链管理等。通过在本数据集上训练机器学习（Machine Learning, ML）与深度学习（Deep Learning, DL）模型，能够实现香蕉品种的精准分类以及成熟度等级的准确评估。此类模型可用于构建自动化系统，以确定最佳采收时机、制定香蕉质量控制标准、分析消费者偏好以指导产品研发与营销策略制定，以及通过优化采收、分拣、包装与库存管理流程来精简供应链。此外，致力于在食品与农业科学领域推进计算机视觉（Computer Vision）技术发展的研究人员，可借助本数据集完善精准农业与食品加工方法。综上，本数据集在实现香蕉生产与加工自动化、降低人力成本以及提升整体运营效率方面具备可观的应用潜力。
备注：本数据集为《BananaImageBD：涵盖不同成熟度等级的孟加拉国常见香蕉品种图像数据集》的更新版本，DOI编号：10.17632/ptfscwtnyz.1

搜集汇总

背景与挑战

背景概述

BananaImageBD是一个全面的香蕉图像数据集，包含孟加拉国四种常见香蕉品种（Bangla Kola、Chompa Kola、Sabri Kola、Sagor Kola）和四个成熟度阶段（绿色、半熟、成熟、过熟）的图像。数据集总共有7,413张增强后的香蕉品种图像和2,457张增强后的成熟度阶段图像，适用于机器学习和深度学习模型训练，用于香蕉品种分类和成熟度评估，应用领域包括精准农业、食品加工和供应链管理。