Adopting New Technology in Coffe Plantation :The Role of Knowledge Sharing in Supply Chain Management

The problem currently faced is that the ground coffee beans are dried and still found many defects, the defects are caused form  sorting system, a good coffee system can increase sales of farmers businesses that initially only sold coffee beans without sorting with simple technology, but with the computerized method of sorting system and sales development will continue to increase with modern computer sorting standards than manual sorting and coffee beans become greenbean. in this study, the application of Computer Vision will be tested to facilitate a sorting process, a computer system with the help of a camera will introduce a type of coffee recognition system to Robusta and Arabica, this system is able to read the number of defects that exist in coffee, this system is able to be implemented and has more accuracy of 90%. coffee that has been sorted automatically will have a good quality and perfect shape of beans and will produce  have unique  aroma and will automatically increase the sale value. This system makes added value for coffee bean farmers, training on knowledge of the sorting system that uses computer applications and practices in running the system supports working farmers to work more efficiently and effectively and provide maximum results in accordance with market needs

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

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

Green coffee beans feature extractor using image processing

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

Elección de características de interés en la clasificación de granos de café mediante un sistema de visión por computadora

RESUMEN La clasificación de la calidad del café antes de ser tostado, una de las operaciones más importantes para definir su calidad y precio en el mercado, se realiza manualmente por personal entrenado en el reconocimiento de los defectos del café. Sin embargo, el carácter subjetivo, costo y tiempo que este involucra genera un campo de investigación importante para la aplicación de tecnologías como la visión artificial. El objetivo de este estudio fue evaluar la capacidad de identificación de defectos y clasificación de granos de café mediante un sistema de visión por computadora en el espacio red-green- blue (RGB). Para este fin se implementó un sistema de adquisición y análisis de imágenes, desarrollando una aplicación informática en Matlab 2015ª. Se compraron en el mercado local muestras de café verde, clasificando cada grano de acuerdo con la NTP 209.027 2001. Se adquirieron las imágenes de cada clase y se analizaron determinando en cada grano seis parámetros de forma, seis parámetros de color, en los espacios RGB y HSV, y dos índices o diferencias normalizadas. Se determinaron los parámetros con influencia estadística en la clasificación mediante software de análisis de datos WEKA y se implementaron tres modelos de clasificación máquinas de soporte vectorial (Support Vector Machine - SVM), arboles de decisión (Decision Tree - DT) y K-vecino más cercano (K-Nearest Neighbor). Los tres tipos de clasificador utilizados en la presente investigación mostraron precisión entre 89% y 92.3% lo cual prueba la posibilidad de implementar sistemas basados en imágenes RGB para clasificar granos de café.ABSTRACT The classification of quality on coffee before toasting, one of the most important operations to define its quality and price in the market, is done manually by personnel trained in the recognition of coffee defects. However, the subjective nature, the cost and the time that it involves generates an important research field for the application of technologies such as artificial vision. The objective of this study was to evaluate the ability to identify defects and classify coffee beans using a computer vision system in the red-green-blue (RGB) space. For this purpose, a system for acquisition and analysis of images was implemented, developing a computer application in Matlab 2015ª. Samples of green coffee were purchased on local market, each grain being classified according to NTP 209.027 2001. Images of each class were acquired and analyzed by determining in each grain six shape parameters, six color parameters, in the RGB and HSV spaces, and two normalized indices or differences. Statistical relevance of parameters was deterined using the software for data analysis named WEKA and using these three models of classification, vector machines (SVM), decision trees and nearest K-neighbor (K-neighbor), were implemented. The three types of classifier used in the present investigation show accuracy between 89% and 92.3% which probe the possibility to implement systems based on RGB image to classify coffee been

Quality evaluation based on color grading - relationship between chemical susbtances and commercial grades by machine version in Corni fructus

Purpose: To evaluate the correlation between the commercial grade of Corni fructus determined by machine vision technology, and its chemical composition. Methods: Loganin, morroniside, ursolic acid, water-soluble extractives, alcohol-soluble extractives, polysaccharides and total organic acids were quantitated in four Corni fructus grades classified by machine vision technology. The content of each component was determined and analyzed bymathematical statistics. Results: Compared with low-grade samples, higher-grade counterparts contained elevated concentrations of alcohol-soluble extractives, water-soluble extractives, loganin and morroniside. In addition, principal component analysis revealed a correlation coefficient of -0.723 between Corni fructus grade and Holistic Scoring based on chemical composition, indicating a significant correlation (p < 0.01). Conclusion: These findings indicated the rationality of the classification method based on machine vision, and further confirmed the notion of  "quality evaluation based on color" of traditional Chinese medicine. Keywords: Corni fructus, Commercial grade, Machine vision technology, Chemical substanc

Artificial Intelligence : Implications for the Agri-Food Sector

Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the agri-food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. This review emphasizes how recent developments in AI technology have transformed the agri-food sector by improving efficiency, reducing waste, and enhancing food safety and quality, providing particular examples. Furthermore, the challenges, limitations, and future prospects of AI in the field of food and agriculture are summarized

Load Cell Mechatronic Approach with Finite Element Analysis (FEA) in SolidWorks Design Development of a Small-Scale Egg Sorter

This research was about developing an automated small-scale egg sorting machine, equipped with mechatronic components, that can sort chicken eggs and place them in trays according to five (5) weight classifications: pewee, small, medium, large, and extra-large with the use of an Arduino load sensor. The machine was made up of a load cell sensor, an Arduino mega controller, a suction mechanism hanging from a rail system driven by a National Electrical Manufacturers Association (NEMA) stepper motor, and a set of five egg trays for the five (5) egg weight classifications. The Arduino Mega microcontroller was used to operate the machine's moving parts, pump, sensors, and LCD. The machine was equipped with an alarm system that produces a sound when the suction picks up the 13th egg of a full tray. The Finite Element Analysis (FEA) simulation was done using SolidWorks software to analyze the vacuum pump capacity and the effects of dynamic force on the eggs during the pick and place process. Testing results of the actual fabricated machine indicated that it was able to successfully weigh, pick through suction, sort, and place eggs into 2x6 trays according to weights. The accuracy was 92.55 percent

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller