Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo

[EN] Artificial vision has wide-ranging applications in the food sector; it is easy to use, relatively low cost and allows to conduct rapid non-destructive analyses. The aim of this study was to use artificial vision techniques to control and model the coffee roasting process. Samples of Castillo variety coffee were used to construct the roasting curve, with captured images at different times. Physico-chemical determinations, such as colour, titratable acidity, pH, humidity and chlorogenic acids, and caffeine content, were investigated on the coffee beans. Data were processed by (i) Principal component analysis (PCA) to observe the aggrupation depending on the roasting time, and (ii) partial least squares (PLS) regression to correlate the values of the analytical determinations with the image information. The results allowed to construct robust regression models, where the colour coordinates (L*, a*), pH and titratable acidity presented excellent values in prediction (R-Pred(2) 0.95, 0.91, 0.94 and 0.92). The proposed algorithms were capable to correlate the chemical composition of the beans at each roasting time with changes in the images, showing promising results in the modelling of the coffee roasting process.Supported by the Universidad Surcolombiana, Project No. USCO-VIPS-3050.Ivorra Martínez, E.; Sarria-González, JC.; Girón Hernández, J. (2020). Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo. Czech Journal of Food Sciences. 38(6):388-396. https://doi.org/10.17221/346/2019-CJFSS38839638

Computer vision for purity, phenol, and pH detection of Luwak Coffee Green Bean

Computer vision as a non-invasive bio-sensing method provided opportunity to detect purity, total phenol, and pH in Luwak coffee green bean. This study aimed to obtain the best Artificial Neural Network (ANN) model to detect the percentage of purity, total phenol, and pH on Luwak coffee green bean by using color features (red-green-blue, gray, hue-saturation-value, hue-saturation-lightness, L*a*b*), and Haralick textural features with color co-occurrence matrix including entropy, energy, contrast, homogeneity, sum mean, variance, correlation, maximum probability, inverse difference moment, and cluster tendency. The best ANN structure was (5 inputs; 30 nodes in hidden layer 1; 40 nodes in hidden layer 2; and 3 outputs) which had training mean square error (MSE) of 0.0085 and validation MSE of 0.0442

Situating Data

Taking up the challenges of the datafication of culture, as well as of the scholarship of cultural inquiry itself, this collection contributes to the critical debate about data and algorithms. How can we understand the quality and significance of current socio-technical transformations that result from datafication and algorithmization? How can we explore the changing conditions and contours for living within such new and changing frameworks? How can, or should we, think and act within, but also in response to these conditions? This collection brings together various perspectives on the datafication and algorithmization of culture from debates and disciplines within the field of cultural inquiry, specifically (new) media studies, game studies, urban studies, screen studies, and gender and postcolonial studies. It proposes conceptual and methodological directions for exploring where, when, and how data and algorithms (re)shape cultural practices, create (in)justice, and (co)produce knowledge

Literary Review: Coffee Technologies

The following article is focused on technologies that can be used to increase or improve the production of coffee. In the modern days the most popular drink can be considered coffee. Its consumption is increasing each year with the increase of population of the planet. Therefore, it is important to use throughout the whole process of getting to the final product of coffee the best available techniques. The objective of this work is to review in the literature different technologies applied to coffee. Authors conclude that technologies that improve crop yields such as artificial intelligence are novel and need to be implemented. On the other hand, the production processes have robust machinery that is well known to coffee growers. Finally, the laboratory technologies to measure the phytochemical qualities of the coffee should be further refined to guarantee the results

OPEN SOURCE ITERATIVE BAYESIAN CLASSIFIER ALGORITHM FOR QUALITY ASSESSMENT OF PROCESSED COFFEE BEANS

ALGORITMO CLASSIFICADOR BAYESIANO ITERATIVO DE CÓDIGO ABERTO PARA AVALIAÇÃO DA QUALIDADE DE GRÃOS DE CAFÉ BENEFICIADOS A seleção de grãos de café desempenha um papel fundamental na qualidade final do produto. Após o processamento, os grãos de café são classificados de acordo com a quantidade de defeitos. Tradicionalmente, essa classificação é executada manualmente, o que torna o processo trabalhoso e demorado. Este problema pode ser resolvido com técnicas de processamento digital de imagens, uma vez que os grãos defeituosos possuem características visuais únicas. Considerando a dificuldade de classificação manual dos defeitos, este trabalho teve como objetivo elaborar um algoritmo classificador bayesiano para identificar esses defeitos em grãos de café beneficiados, com base em sua forma e cor. Para tal, foram utilizados 630 grãos de café arábica, somando oito imagens ao todo. O algoritmo objetivou classificar quatro classes, que foram: grãos normais, grãos normais quebrados, grãos pretos e grãos pretos quebrados. Para avaliar a precisão do algoritmo classificador, calculou-se a exatidão global e o coeficiente Kappa, o que permite inferir se o classificador é melhor que uma classificação aleatória. Concluiu-se que o algoritmo desenvolvido apresentou uma precisão global de 76% e kappa igual a 0,6. Além disso, a metodologia proposta mostrou grande potencial para aplicação na avaliação da qualidade de outros produtos, cujos parâmetros de forma e espectrais são relevantes na avaliação de sua qualidade.Palavras-chave: qualidade de grãos de café; processamento digital de imagens; Jupyter Notebook; classificação supervisionada. ABSTRACT: The selection of coffee beans plays a key role in the product's final quality. After processing, coffee beans are classified according to their quantity of defects. Traditionally this classification is performed manually, which makes the process laborious and time-consuming. This problem can be solved with digital image processing techniques since defective grains have unique visual characteristics. Considering the difficulty of manual classification of the defects, this study aimed to elaborate a Bayesian classifier algorithm to identify these defects in benefited coffee beans, based on its shape and color. To do so, 630 grains of arabica coffee were used, composing eight images in total. The algorithm aimed to classify four classes, which were: regular beans, normal broken beans, black beans, and black broken beans. In order to evaluate the accuracy of the classifier algorithm, it was calculated the global accuracy and the Kappa coefficient, which allows inferring if the classifier is better than a random classification. It was concluded that the developed algorithm presented a global accuracy of 76% and kappa equals to 0.6. Also, the proposed methodology showed great potential for application in the quality evaluation of other products, whose shape and spectral parameters are relevant in evaluating its quality.Keywords: coffee beans quality; digital image processing; Jupyter Notebook; supervised classification

Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

Coffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983

Situating Data: Inquiries in Algorithmic Culture

Taking up the challenges of the datafication of culture, as well as of the scholarship of cultural inquiry itself, this collection contributes to the critical debate about data and algorithms. How can we understand the quality and significance of current socio-technical transformations that result from datafication and algorithmization? How can we explore the changing conditions and contours for living within such new and changing frameworks? How can, or should we, think and act within, but also in response to these conditions? This collection brings together various perspectives on the datafication and algorithmization of culture from debates and disciplines within the field of cultural inquiry, specifically (new) media studies, game studies, urban studies, screen studies, and gender and postcolonial studies. It proposes conceptual and methodological directions for exploring where, when, and how data and algorithms (re)shape cultural practices, create (in)justice, and (co)produce knowledge

Spartan Daily, March 23, 1993

Volume 100, Issue 37https://scholarworks.sjsu.edu/spartandaily/8395/thumbnail.jp