Work

GCBICT: Green Coffee Bean Identification Command-line Tool

Year: 2024

Type: software-paper

Abstract: 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.

Source: SoftwareX

DOI: 10.1016/j.softx.2024.101843

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

Institutions: National Tsing Hua University, National Center for Theoretical Sciences

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $970