Image analysis for classifying coffee bean quality using a multifeature and machine learning approach

Aug 5, 2026

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Coffee bean quality directly influences pricing, scarcity effects, and consumer satisfaction, yet growers and shop owners often struggle to grade beans reliably due to visual limits, fatigue, and inconsistency. This study proposes a computer-vision pipeline for robusta coffee bean classification, covering image acquisition, ROI detection, pre-processing, segmentation, feature extraction, feature selection, and machine-learning classification. Color, shape, and texture features are combined, feature selection is performed using PCA, and performance is evaluated by precision, recall, and accuracy, with the BPNN classifier achieving 98.54% accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Computer vision in agriculture and coffee

Quality grading challenges

Related work and methods

Image analysis for classifying coffee bean quality using a multifeature and machine learning approach Anindita Septiarini1, Hamdani Hamdani1, Aji Ery Burhandeny2, Damar Nurcahyono3, Surya Eka Priyatna4 1Department of Informatics, Faculty of Engineering, Mulawarman University, Samarinda, Indonesia 2Department of Electronic Engineering, Faculty of Engineering, Mulawarman University, Samarinda, Indonesia 3Department of Information Technology, Politeknik Negeri Samarinda, Samarinda, Indonesia 4Department of Information Technology, Faculty of Da'wah and Communication Sciences, Antasari State Islamic University, Banjarmasin, Indonesia Article history: Received Nov 27, 2023 Revised Feb 11, 2024 Accepted Feb 28, 2024 Keywords: Coffee beans Features selection K-means Machine learning Principal component analysis Corresponding Author: Price and customer satisfaction depend on coffee bean quality. The coffee industry must analyze coffee bean quality. Global demand for robusta coffee is high. Coffee industry professionals mostly understand coffee bean quality. Thus, an image analysis using a computer vision-based approach for classifying robusta coffee bean quality is required. Image acquisition, region of interest (ROI) detection, pre-processing, segmentation, feature extraction, feature selection, and classification are covered in this study. A multi-feature derived based on color, shape, and texture features was employed in feature extraction, followed by feature selection using principal component analysis (PCA) . Several machine-learning methods classified the coffee beans. The method performance was assessed using precision, recall, and accuracy. The selected features using the backpropagation neural network (BPNN) classifier outperformed others with 98.54% accuracy. This is an open access article under the CC BY-SA license. Anindita Septiarini Department of Informatics, Faculty of Engineering, Mulawarman University St. Sambaliung, No. 9, Samarinda, Indonesia Email: [anindita@unmul.ac.id](anindita@unmul.ac.id) Article Info ABSTRACT 1. INTRODUCTION The utilization of computers and associated technologies is seeing fast expansion and diversification. The application of this is being observed in the field of agriculture. There exist multiple instances wherein computers have been employed in the agricultural sector, encompassing the monitoring of fruit ripeness [1], [2], land management [3], and plant development [4], [5] . Coffee, as one of the most widely consumed beverages globally, holds significant importance as an economic commodity. The global popularity of coffee can be attributed to its stimulating properties and the preference for its bitter flavor. Coffee serves as a substantial provider of caffeine for a considerable number of individuals. While previous research has established a connection between coffee and caffeine intake and adverse health effects, recent studies have presented evidence suggesting that the compounds found in coffee, such as caffeine, chlorogenic acids, kahweol, cafestol, and various micronutrients (such as magnesium, potassium, and phosphorus), may enhance the immune system and provide protection against the development of conditions such as obesity, diabetes, neurological diseases, osteoporosis, and pancreatic cancer [6] . The coffee industry values quality because of the relationship between coffee bean scarcity, monetary compensation, and consumer happiness. Robusta coffee beans, widely grown, have a distinct taste and aroma. Quality of robusta coffee beans depends on soil makeup, climate, and processing method. Coffee prices depend on bean quality. It is crucial to note that not all growers and coffee shop owners can identify coffee bean quality. Thus, errors may occur when they lack this expertise. Grading is time-consuming and produces inconsistent outcomes. Due to visual perception limits, fatigue, and coffee quality evaluation differences, these inconsiste

FAQs

What is the main goal of the study?

To classify robusta coffee bean quality using an image analysis approach combined with multifeature extraction and machine learning.

Which features and selection method are used?

The approach extracts color, shape, and texture features, then applies principal component analysis (PCA) for feature selection.

How is classification performance measured and what is the best result?

Performance is assessed using precision, recall, and accuracy. The backpropagation neural network (BPNN) classifier performs best with 98.54% accuracy.

Aug 5, 2026

Upload by Miles