Coffee Bean Grade Determination Based on Image Parameter

Quality standard for coffee as an agriculture commodity in Indonesia uses defect system which is regulated in Standar Nasional Indonesia (SNI) for coffee bean, No: 01-2907-1999. In the Defect System standard, coffee bean is classified into six grades, from grade I to grade VI depending on the number of defect found in the coffee bean. Accuracy of this method heavily depends on the experience and the expertise of the human operators. The objective of the research is to develop a system to determine the coffee bean grading based on SNI No: 01-2907-1999. A visual sensor, a webcam connected to a computer, was used for image acquisition of coffee bean image samples, which were placed under uniform illumination of 414.5+2.9 lux. The computer performs feature extraction from parameters of coffee bean image samples in the term of texture (energy, entropy, contrast, homogeneity) and color (R mean, G mean, and B mean) and determines the grade of coffee bean based on the image parameters by implementing neural network algorithm. The accuracy of system testing for the coffee beans of grade I, II, III, IVA, IVB, V, and VI have the value of 100, 80, 60, 40, 100, 40, and 100%, respectively

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

Clasificación automática de tipos de semilla de quinua a través de descriptores de color

Los agricultores de quinua para obtener cosechas optimas deben seleccionar de manera adecuada sus semillas a cultivar sin que se mezclen con otras variedades. La investigación actual se centra en la clasificación automática de tres tipos de semillas de quinua (Sacaca, Pasankalla y Salcedo) utilizando descriptores de color. Después de la adquisición de imágenes de semilla de quinua, se les asigna el filtro Gaussianblur para corregir y cuantificar el color en las imágenes permitiendo resaltar las diferencias entre las características de cada tipo de semilla de quinua. Las imágenes suavizadas se asignan al proceso de segmentación utilizando el método de Otsu para extraer después las características y realizar el entrenamiento de los clasificadores. Para realizar la clasificación de las semillas de quinua se utilizó SVM mediante el análisis lineal pixel a pixel. Los resultados de ensayo demuestran que el procedimiento de desarrollo tiene una alta precisión

Karakterisasi Kematangan Buah Kopi Berdasarkan Warna Kulit Kopi Menggunakan Histogram dan Momen Warna

Conventionally, the coffee maturity level is determined by observing the fruit colour, and it is done manually. This approach may result in inconsistency in colour classification. Thus, an automatic colour classification method based on colour of coffee maturity level is required. This paper presents the characterization of coffee maturity level based on two colour features: colour histogram and colour moment. Characterization of coffee maturity level was grouped into four class: green for unripe coffee, greenish-yellow for half ripe coffee, red for ripe coffee, and dark red for too ripe coffee. The purpose of the research is to determine the colour features that can characterize the coffee maturity level based on computer simulation in extracting and calculating the statistical values of the colour histogram and colour moments. It turned out from 200 coffee images that the statistical values of colour histogram are more suitable for characterising the coffee maturity. The kurtosis values of hue histogram for each maturity level of coffee were different: kurtosis value of unripe coffee was 17.2-28.3, those of half ripe coffee, ripe coffee and too ripe coffee were 29.2-31.4, 32.7-83.5, and more than 84.2 respectively..Keywords : colour histogram kurtosis, colour moment, image processing.AbstrakSecara tradisional, tingkat kematangan buah kopi ditentukan dari warna kulitnya yang dikelompokan secara manual. Cara ini menghasilkan pengelompokan warna yang kurang konsisten, sehingga diperlukan sebuah metode otomatis pengelompokan buah kopi berdasarkan warna dari tingkat kematangannya. Penelitian ini memaparkan hasil karakterisasi kematangan buah kopi arabika menggunakan dua fitur warna citra, yaitu histogram dan momen warna. Karakterisasi kematangan dibagi menjadi empat kelompok: hijau untuk kopi muda, hijau kekuningan untuk kopi setengah masak, merah untuk kopi masak, dan merah tua untuk kopi tua. Tujuan penelitian ini adalah menentukan fitur warna yang dapat mewakili karakter kematangan buah kopi dengan melakukan simulasi komputer untuk mengekstrak dan menghitung nilai statistik dari histogram warna dan nilai momen warna dari empat kelompok buah kopi.  Hasil penelitian menggunakan 200 citra kopi menunjukkan bahwa nilai statistik dari histogram warna lebih menggambarkan karakter kematangan buah kopi, dibandingkan dengan momen warna. Nilai kurtosis dari histogram hue memiliki nilai berbeda untuk setiap kategori kematangan buah kopi: kopi muda memiliki nilai kurtosis 17.2-28.3, kopi setengah masak 29.2-31.4, kopi masak 32.7-83.5dan kopi tua lebih dari 84.2.  Kata Kunci : kurtosis histogram warna, momen warna, pengolahan citra

Karakterisasi Kematangan Buah Kopi Berdasarkan Warna Kulit Kopi Menggunakan Histogram dan Momen Warna

Conventionally, the coffee maturity level is determined by observing the fruit colour, and it is done manually. This approach may result in inconsistency in colour classification. Thus, an automatic colour classification method based on colour of coffee maturity level is required. This paper presents the characterization of coffee maturity level based on two colour features: colour histogram and colour moment. Characterization of coffee maturity level was grouped into four class: green for unripe coffee, greenish-yellow for half ripe coffee, red for ripe coffee, and dark red for too ripe coffee. The purpose of the research is to determine the colour features that can characterize the coffee maturity level based on computer simulation in extracting and calculating the statistical values of the colour histogram and colour moments. It turned out from 200 coffee images that the statistical values of colour histogram are more suitable for characterising the coffee maturity. The kurtosis values of hue histogram for each maturity level of coffee were different: kurtosis value of unripe coffee was 17.2-28.3, those of half ripe coffee, ripe coffee and too ripe coffee were 29.2-31.4, 32.7-83.5, and more than 84.2 respectively

Computer Vision-Aided Intelligent Monitoring of Coffee: Towards Sustainable Coffee Production

Coffee which is prepared from the grinded roasted seeds of harvested coffee cherries, is one of the most consumed beverage and traded commodity, globally. To manually monitor the coffee field regularly, and inform about plant and soil health, as well as estimate yield and harvesting time, is labor-intensive, time-consuming and error-prone. Some recent studies have developed sensors for estimating coffee yield at the time of harvest, however a more inclusive and applicable technology to remotely monitor multiple parameters of the field and estimate coffee yield and quality even at pre-harvest stage, was missing. Following precision agriculture approach, we employed machine learning algorithm YOLO, for image processing of coffee plant. In this study, the latest version of the state-of-the-art algorithm YOLOv7 was trained with 324 annotated images followed by its evaluation with 82 unannotated images as test data. Next, as an innovative approach for annotating the training data, we trained K-means models which led to machine-generated color classes of coffee fruit and could thus characterize the informed objects in the image. Finally, we attempted to develop an AI-based handy mobile application which would not only efficiently predict harvest time, estimate coffee yield and quality, but also inform about plant health. Resultantly, the developed model efficiently analyzed the test data with a mean average precision of 0.89. Strikingly, our innovative semi-supervised method with an mean average precision of 0.77 for multi-class mode surpassed the supervised method with mean average precision of only 0.60, leading to faster and more accurate annotation. The mobile application we designed based on the developed code, was named CoffeApp, which possesses multiple features of analyzing fruit from the image taken by phone camera with in field and can thus track fruit ripening in real time

Aplicación de técnicas de visión por computador en la selección de palta hass de calidad

La calidad de la Palta Hass, es uno de las especificaciones más importantes en el desarrollo del cultivo nacional de exportación. Las deficiencias estudiadas fueron los frutos enteros y no enteros, frutos con queresas, frutos con defectos mayores (quemaduras y plagas) y defectos menores (Color); El objetivo del presente trabajo de investigación fue determinar e identificar los frutos aceptables en el entorno de calidad identificando sus principales problemas de rechazo por los clientes por medio de visión artificial, utilizando procesamiento de imágenes y descriptores de color (de los cuales se ha usado descriptores K-Means); se utilizó un total de 1260 imágenes, basado en 210 frutos distribuidos en tres niveles. Se obtienen seis imágenes por cada fruto, en la etapa de procesamiento y filtros de creación propia en la etapa de pre-procesamiento. Los descriptores se usaron con el fin de extraer características de todas las imágenes para la etapa de entrenamiento del sistema, luego de haber extraído los vectores a todas las muestras, luego se clasifican las muestras de prueba mediante Segment Color, Segment Rose y K-Means, como resultado general resulta el clasificador KNN como el más óptimo para la clasificación obteniendo un 77% de acierto.TesisInfraestructura, Tecnología y Medio Ambient