Site-Specific Color Features of Green Coffee Beans
This addresses the need for efficient quality evaluation in the coffee industry, though it appears incremental as it builds on existing evaluation schemes with specific improvements.
The paper tackles the labor-intensive and subjective visual inspection of green coffee bean quality by developing a site-independent method to identify site-specific color features of the seed coat, which enables simple, low-cost, and universally applicable evaluation schemes that can distinguish qualified beans from different growing sites and prevent cheating.
Coffee is one of the most valuable primary commodities. Despite this, the common selection technique of green coffee beans relies on personnel visual inspection, which is labor-intensive and subjective. Therefore, an efficient way to evaluate the quality of beans is needed. In this paper, we demonstrate a site-independent approach to find site-specific color features of the seed coat in qualified green coffee beans. We then propose two evaluation schemes for green coffee beans based on this site-specific color feature of qualified beans. Due to the site-specific properties of these color features, machine learning classifiers indicate that compared with the existing evaluation schemes of beans, our evaluation schemes have the advantages of being simple, having less computational costs, and having universal applicability. Finally, this site-specific color feature can distinguish qualified beans from different growing sites. Moreover, this function can prevent cheating in the coffee business and is unique to our evaluation scheme of beans.