AN EFFICIENT REAL-TIME ALGORITHM USING SHAPE AND CIELAB COLOR SPACE FOR SORTING COFFEE BEANS
Keywords:Coffee bean, sorting coffee bean, shape and color for sorting, machine learning for sorting, CIELab color space
Sorting coffee beans is a crucial stage to achieve high quality and raise the value for the product. This work usually takes a short time to conduct with a large number of coffee beans, while sorting by hand is hard to respond to. And in some cases, appearances of bad coffee beans are nearly similar to good ones, this is hard to distinguish by eyes as sorting in bulk. So an efficient algorithm used particular standards to sort coffee is necessary. From existed issues, this paper presents an efficient approach used as a computer vision system to sort coffee beans based on the criteria about shape and color of the product. Geometric properties and a linear graph are used in this paper to analyze the features of the product. Coffee beans are categorized into two major groups: bad beans and good beans, corresponding to quality standards about specific color and shape. Our proposed method detects and covers the majority of types of bad beans, and get high at both the accuracy metric and F1-score metric with fast speed in sorting.