B J Bipin Nair, K M Abrav Nanda, A S Shalwin, Raghavendra V
Department of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, Karnataka, India.
Data Brief. 2025 Mar 3;59:111434. doi: 10.1016/j.dib.2025.111434. eCollection 2025 Apr.
The development of advanced coffee bean classification techniques depends on the availability of high quality datasets. Coffee bean quality is influenced by various factors, including bean size, shape, colour, and defects such as fungal damage, full black, full sour, broken beans, and insect damage. Constructing an accurate and reliable ground truth dataset for coffee bean classification is a challenging and labour intensive process. To address this need, we introduce the Coffee Beans Dataset (CBD) which contains 450 high-resolution images sampled across 9 distinct coffee bean grades A, AA, AAA, AB, C, PB-I, PB-II, BITS and BULK with 50 images per class. These samples were sourced from Wayanad, Kerala, reflecting the region's diverse coffee bean quality .This dataset is specifically designed to support machine learning and deep learning models for coffee bean classification and grading. By providing a comprehensive and diverse dataset, we aim to address key challenges in coffee quality assessment and improvement in classification accuracy. When tested using the EfficientNet-B0 model, the model achieved a high accuracy of 100%, demonstrating its potential to enhance automated coffee bean grading systems. The CBD serves as a valuable resource for researchers and industry professionals, promot-ing innovation in coffee quality monitoring and classification algorithms.
先进咖啡豆分类技术的发展依赖于高质量数据集的可用性。咖啡豆品质受多种因素影响，包括豆子大小、形状、颜色以及诸如真菌损伤、全黑、全酸、碎豆和虫害等缺陷。为咖啡豆分类构建一个准确可靠的真值数据集是一个具有挑战性且耗费人力的过程。为满足这一需求，我们引入了咖啡豆数据集（CBD），它包含450张高分辨率图像，这些图像取自9个不同的咖啡豆等级：A、AA、AAA、AB、C、PB - I、PB - II、BITS和BULK，每个等级有50张图像。这些样本来自喀拉拉邦的韦亚纳德，反映了该地区多样的咖啡豆品质。这个数据集专门设计用于支持用于咖啡豆分类和分级的机器学习和深度学习模型。通过提供一个全面且多样的数据集，我们旨在应对咖啡品质评估中的关键挑战并提高分类准确性。当使用EfficientNet - B0模型进行测试时，该模型达到了100%的高精度，证明了其在增强自动化咖啡豆分级系统方面的潜力。CBD为研究人员和行业专业人士提供了宝贵资源，推动了咖啡品质监测和分类算法的创新。
