Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System

Abstract

There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the k-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.

Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging

“…Table 1 tabulates the green coffee bean evaluation methods proposed by previous studies. Origin classification 955-1700 nm (266 bands) 432 beans PLS + SVM 97.1% [3] Origin classification 900-1700 nm (256 bands) 1200 beans SVM 80% [4] Sour beans, black beans, broken beans RGB 444 beans k-NN 95.66% [5] Black beans RGB 180 beans Threshold (TH) 100% [6] In 2019, Oliveri et al [2] used VIS-NIR to identify the black beans, broken beans, dry beans, and dehydrated coffee beans using principal component analysis (PCA) and the k-nearest neighbors algorithm (k-NN) for classification. Although their method can extract effective wavebands, the disadvantages are that the recognition rate is only 90%.…”

“…There have been a few reports on traditional RGB images. García [5] used K-NN to classify sour beans, black beans, and broken beans. The limitations of the method are that K-NN is white (99% reflection spectrum) images were recorded and stored automatically before each measurement.…”

“…However, in that paper, the number of beans was relatively too small, and final detection rate was lower than the proposed method of this paper. Other studies [5,6] have used traditional RGB images, which can only address targets according to the color and shape based on the spatial information, meaning that it can only identify black and broken beans. In contrast with prior studies, HIDDA is based on spectral information provided by hyperspectral sensors, which can detect targets at the subpixel level of insect damage, which has very limited spatial information.…”

Quality assessment of coffee beans through computer vision and machine learning algorithms

“…For coffee beans defects classification, shape and color descriptors are some of the commonly used criteria (Farah et al, 2006;Franca et al, 2005;Toci;Farah, 2008), in which some defective beans and extraneous matter can correlate with them (Faridah;Parikesit;Ferdiansjah, 2011;Oliveira et al, 2016;Santos et al, 2020). In this sense, some machine learning models have been proposed to classify coffee beans defects, such as convolutional neural network (Fukai et al, 2018) and k-nearest neighbour (García;Candelo-Becerra;Hoyo, 2019) algorithms. However, the low accuracy in classifying specific types of coffee beans defects such as sour (García;Candelo-Becerra;Hoyo, 2019), fade, broken and peaberry beans (Fukai et al, 2018) were pointed as their main limitations.…”

“…In this sense, some machine learning models have been proposed to classify coffee beans defects, such as convolutional neural network (Fukai et al, 2018) and k-nearest neighbour (García;Candelo-Becerra;Hoyo, 2019) algorithms. However, the low accuracy in classifying specific types of coffee beans defects such as sour (García;Candelo-Becerra;Hoyo, 2019), fade, broken and peaberry beans (Fukai et al, 2018) were pointed as their main limitations. Although some studies prove the effectiveness of some machine learning models to access coffee beans quality, information about the features' importance for the classifiers are scarce.…”