An image processing technique for coffee black beans identification

Arboleda, Edwin R.; Fajardo, Arnel C.; Medina, Ruji P.

doi:10.1109/icird.2018.8376325

Cited by 33 publications

(26 citation statements)

References 18 publications

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“…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%.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Chang

et al. 2020

Remote Sensing

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The defective beans of coffee are categorized into black beans, fermented beans, moldy beans, insect damaged beans, parchment beans, and broken beans, and insect damaged beans are the most frequently seen type. In the past, coffee beans were manually screened and eye strain would induce misrecognition. This paper used a push-broom visible-near infrared (VIS-NIR) hyperspectral sensor to obtain the images of coffee beans, and further developed a hyperspectral insect damage detection algorithm (HIDDA), which can automatically detect insect damaged beans using only a few bands and one spectral signature. First, by taking advantage of the constrained energy minimization (CEM) developed band selection methods, constrained energy minimization-constrained band dependence minimization (CEM-BDM), minimum variance band prioritization (MinV-BP), maximal variance-based bp (MaxV-BP), sequential forward CTBS (SF-CTBS), sequential backward CTBS (SB-CTBS), and principal component analysis (PCA) were used to select the bands, and then two classifier methods were further proposed. One combined CEM with support vector machine (SVM) for classification, while the other used convolutional neural networks (CNN) and deep learning for classification where six band selection methods were then analyzed. The experiments collected 1139 beans and 20 images, and the results demonstrated that only three bands are really need to achieve 95% of accuracy and 90% of kappa coefficient. These findings show that 850–950 nm is an important wavelength range for accurately identifying insect damaged beans, and HIDDA can indeed detect insect damaged beans with only one spectral signature, which will provide an advantage in the process of practical application and commercialization in the future.

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Section: Introductionmentioning

confidence: 99%

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

Chen

Chang

et al. 2020

Remote Sensing

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“…Image processing is one of the most promising techniques to extract green coffee bean features because it does not require complicated sensors and highly technical electronics [26,27]. It only needs a camera, a lighting mechanism, a computer or a microcontroller and a good algorithm for feature extraction [28,29].…”

Section: Introductionmentioning

confidence: 99%

Green coffee beans feature extractor using image processing

2020

Self Cite

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This study offers a novel solution to deal with the low signal-to-noise ratio and slow execution rate of the first derivative edge detection algorithms namely, Roberts, Prewitt and Sobel algorithms. Since the two problems are brought about by the complex mathematical operations being used by the algorithms, these were replaced by a discriminant. The developed discriminant, equivalent to the product of total difference and intensity divided by the normalization values, is based on the "pixel pair formation" that produces optimal peak signal to noise ratio. Results of the study applying the discriminant for the edge detection of green coffee beans shows improvement in terms of peak signal to noise ratio (PSNR), mean square error (MSE), and execution time. It was determined that accuracy level varied according to the total difference of pixel values, intensity, and normalization values. Using the developed edge detection technique led to improvements in the PSNR of 2.091%, 1.16 %, and 2.47% over Sobel, Prewitt, and Roberts respectively. Meanwhile, improvement in the MSE was measured to be 13.06%, 7.48 %, and 15.31% over the three algorithms. Likewise, improvement in execution time was also achieved at values of 69.02%, 67.40 %, and 65.46% over Sobel, Prewitt, and Roberts respectively.

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“…The authors in these papers only consider the color to grade the grain quality by its maturation stages, ignoring important defects such as small, very long berry or broken beans that are considered in the present manuscript and that are not detected by maturation stages identification. In addition, some conferences have reported machine vision systems for the classification of green coffee beans [24], for characterizing coffee beans from different towns [25], for automatic classification of defects in green coffee beans [26], for coffee black beans identification [27] and for the recognition of defects in coffee beans [28].…”

Section: Introductionmentioning

confidence: 99%

“…A low accuracy for the recognition of defects in coffee beans is presented in [28] and a low accuracy for the inspection of fade and broken beans is presented in [24]. Furthermore, in [27] only black beans are identified. Despite key technology towards the development of a machine vision for coffee beans inspection, few techniques have been presented to select green coffee beans and consider several physical characteristics for quality identification.…”

Section: Introductionmentioning

confidence: 99%

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

García

Candelo-Becerra²,

Hoyos

2019

Applied Sciences

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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.

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An image processing technique for coffee black beans identification

Cited by 33 publications

References 18 publications

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

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

Green coffee beans feature extractor using image processing

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

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