CBD: Coffee Beans Dataset

TL;DR

The paper introduces a high-resolution coffee bean dataset with 450 images across nine grades to improve machine learning classification accuracy.

Contribution

The novel contribution is the creation of the Coffee Beans Dataset (CBD) with diverse, high-quality images for accurate classification and grading.

Findings

The CBD contains 450 images across nine coffee bean grades with 50 images per class.

The EfficientNet-B0 model achieved 100% accuracy when tested on the dataset.

Abstract

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.

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