Classification of coffee bean varieties based on a deep learning approach

D. Buonocore, M. Carratù, M. Lamberti

Abstract

In this article, a coffee beans fraud detection based on a deep learning approach is proposed, which has been achieved after classifying the two coffee varieties to distinguish them in a real-time industrial scenario. The coffee bean quality is typically defined by visual inspection, which is subjective, needing significant effort and time, and susceptible to fault detection. For these reasons, a different method is required to be objective and precise. Therefore, object detection techniques were employed to automatically classify the coffee bean samples according to their specie using an own dataset consisting of over 2500 coffee beans. Furthermore, a convolutional neural network (CNN) based on the YOLO algorithm was employed to categorize the coffee beans automatically. The result of this study has revealed that the object detection technique could be used as an effective method to classify coffee bean species and discover food fraud.

Keywords

Deep Learning; Food Quality; Coffee Beans; Fraud Detection; Classification; YOLOv5; Object Detection; Industry Innovation and Infrastructure; SDG 9

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IMEKO-TC10-2022-002.pdf

DOI

10.21014/tc10-2022.002

IMEKO TC

TC10 - Measurement for Diagnostics, Optimization and Control

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