GCBICT：绿咖啡豆识别命令行工具

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2024-09-26 DOI:10.1016/j.softx.2024.101843

Shu-Min Tan , Shih-Hsun Hung , Je-Chiang Tsai

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引用次数: 0

摘要

咖啡是商品市场上最重要的农产品之一。咖啡饮料的质量在很大程度上取决于绿色咖啡豆的质量。然而，传统的挑选技术主要依赖于人员的目测，主观且耗时。基于我们最近发现的绿咖啡豆座衣的特定地点颜色特征和支持向量机（一种机器学习分类器），基于 Python 的咖啡豆识别/评估方案 GCBICT 为识别合格咖啡豆及其种植地点提供了一种经济、有效和用户友好的方法。合格-次品分离器的功能是区分合格和次品咖啡豆。由于咖啡豆颜色特征的特定地点属性，训练集可能较小。如果咖啡经销商为了降低成本而将来自不同种植地的咖啡豆混合在一起，混合分离器就能识别出合格的咖啡豆。此外，这一功能也是我们的评估方案所独有的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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GCBICT: Green Coffee Bean Identification Command-line Tool

Coffee is one of the most important agricultural commodities in commodity markets. The quality of coffee beverages strongly depends on that of green coffee beans. However, the conventional selection technique mainly relies on personnel visual inspection, which is subjective and time-consuming. Based on our recently discovered site-specific color characteristics of the seat coat of green coffee beans and support vector machines (a machine learning classifier), the Python-based identification/evaluation scheme of beans, GCBICT, provides an affordable, effective, and user-friendly way to identify qualified beans and their growing sites.

The command-line tool consists of two functions: (1) the Qualified-Defective Separator and (2) the Mixed Separator. The Qualified-Defective Separator function is to distinguish between qualified and defective green coffee beans. Due to the site-specific property of our color characteristics of beans, the training set can be small. The Mixed Separator can identify qualified beans from different growing sites if coffee distributors mix them for cost in their business. Moreover, this function is unique to our evaluation scheme.

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.