Sorting Machine Research Articles

Preliminary study non-destructive sorting techniques for pepper (Capsicum annuum L.) using odor parameter

Almost all fruits and vegetables sold in modern society are sorted and labeled, making it easier for customers to recognize the quality of the product, leading to more regular distribution and supply. Consequently, it facilitates the initial packaging and transportation of the product, and farmers will benefit from the added value. Therefore, it is necessary to develop sorting via affordable machines and easy to operate at the current technology level. Since electronic nose technology is new-emerging, it can be used in food quality control systems. In this study, the variety Padrón (Capsicum annuum L.) was evaluated. PCA, LDA, SVM and ANN methods were used to classify sweet and hot peppers. According to PCA, 98% of the variance in the data was detected by the first three components. SVM, ANN, and LDA all showed 100% accuracy in classification. The amounts of capsaicin in two types of sweet and hot peppers were predicted well and with high accuracy by three different methods: MLR, PCR, and PLSR. With this method, it is possible to reliably separate sweet and hot peppers based on odor parameters, and it is also possible to develop sorting machines according to the characteristics of odor.

The Use of Image Processing and Sensor in Tomato Sorting Machine by Color, Size, and Weight

Tomatoes are a popular vegetable in Indonesia, where production increases every year according to market demand. The large production requires proper post-harvest handling both in quality and time. It has been well-known that sorting and grading are the first and foremost processes in the post-harvest process of tomatoes. Sorting tomatoes can be conducted by color and adjusted to the target market. The automation process in the post-sorting and grading process can save time and resources. This research proposes a sorting system that sorts tomatoes based on color, size, and weight. Tomatoes are sorted by red, yellow, and green colors. The detection of color and size was carried out by image processing with the OpenCV library. The color detection was carried out by HSV's red, yellow, and green values. In comparison, the dimensional measurement was carried out by determining the outermost point of the detected object both vertically and horizontally. At the same time, tomatoes' weight was measured by a weight sensor. This system was implemented into a prototype sorting system with a webcam, Arduino, a conveyor, and motors. The final part was a storage box used to accommodate tomatoes based on grading. The implementation has the maximum results for detecting color with 100% accuracy and measuring weight with 95% accuracy. However, it still needs development for dimensional measurements. In this research, it has only 5% accuracy. This proposed system can be used both in software and hardware design as an inline tomato sorting tool.

A machine vision-intelligent modelling based technique for in-line bell pepper sorting

The uniformity of appearance attributes of bell peppers is significant for consumers and food industries. To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and separating the not likable low-color bell peppers, developing an appropriate sorting system would be of high importance and influence. According to standards and export needs, the bell pepper should be graded based on maturity levels and size to five classes. This research has been aimed to develop a machine vision-based system equipped with an intelligent modelling approach for in-line sorting bell peppers into desirable and undesirable samples, with the ability to predict the maturity level and the size of the desirable bell peppers. Multilayer perceptron (MLP) artificial neural networks (ANNs) as the nonlinear models were designed for that purpose. The MLP models were trained and evaluated through five-fold cross-validation method. The optimum MLP classifier was compared with a linear discriminant analysis (LDA) model. The results showed that the MLP outperforms the LDA model. The processing time to classify each captured image was estimated as 0.2 s/sample, which is fast enough for in-line application. Accordingly, the optimum MLP model was integrated with a machine vision-based sorting machine, and the developed system was evaluated in the in-line phase. The performance parameters, including accuracy, precision, sensitivity, and specificity, were 93.2%, 86.4%, 84%, and 95.7%, respectively. The total sorting rate of the bell pepper was also measured as approximately 3 000 samples/h.

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The article gives a brief scientific basis for the possibility of using microfocus radiography in the confectionery industry and presents a diagram of the optical sorting machine.

Colour Based Object Sorting Machine

Sorting is a process in which two or more objects of similar, yet different characteristics are arranged in a systematic order. This is generally carried through manually or by using sensors in automation. Automatic color sorting is very much convenient in industry. Color and size are the most important features for accurate classification and sorting of product which can be done by using some optical sensors or analyzing their pictures. Color sorting machines are machines that are used on the production lines in bulk food processing and other industries. They separate items by their colors, detecting the colors if things that pass before them and using mechanical or pneumatic ejection devices to divert items whose colors do not fall within the acceptable range. The Color sorting machine using microcontroller is a fascinating and renowned project for techies, who would like to combine electronics, machine building and programming. The Color Sorting Machine is used for sorting mainly RGB colors. A simple robot arm is used to apply a color sorting to a physical system. The objects are placed to the conveyor belt using robot arm with servo motors. One conveyor belt is used, which is controlled by DC motors.

Color Sorting System Using Arduino Nano

Abstract: Sorting of object is a vital mechanical method within which troublesome work is kind of required. Chronic manual arrangement makes consistency troubles. Machines will perform in the main dreary assignments superior to human beings. Working man exhaustion on ordered producing structures may {end up} in reduced execution, and purpose troubles in holding up object fine. A worker who has been showing analysis enterprise over and over could in addition within the end ditch to acknowledge the color of tem, however a machine in no way. In this paper we have describe on the sorting machine which can be help to sort the products. There’s a large usage of the many product in our day to day life, associate degreed producing of this products are tired many large scale and tiny scale industries. Arrangement makes quality consistency issue. Today the most issue that's visage when the assembly is of sorting arrangement of things in an trade may be a uninteresting trendy process, that is by and huge done physically. We are implementing the color sorting machine with the new feature which can count the marbles as well as detect. Keywords: Color Sorting, TCS3200, Arduino Nano, LCD display.

Design and simulation of an artificial vision sorting machine and cocoa pulping machine

This research project is focused on the design of an agricultural machine that allows to pulp the cocoa beans quickly and optimally by means of an artificial vision system to classify the beans and to obtain the mentioned product in a shorter production time with optimal quality. Cocoa has been placed in third place in production after sugar and coffee, this is due to the growing demand for chocolate, which is one of the most consumed derivatives in the world. Prior to the design, the manual and traditional methods used on the farms were investigated in order to take a mechanical design alternative. Of the three alternatives proposed, it was decided to take as an example alternative C, using a sieve with a constant translational movement. This machine was designed with the help of SOLIDWORKS software, allowing to obtain plans and dimensions suitable for its construction. A finite element analysis was performed to evaluate its deformations with the loads to which it will be subjected. The design of this machine aims to open new fields in agricultural production and maximize the income of small farmers, taking advantage of the reduction of labor used in the cocoa pulping process.

Mathematical Description and Study of the Vibration Deck of a Grain Sorting Machine

This article presents the results of a study of the vibrations of the deck of a pneumatic sorting machine. The purpose of this work is to describe the design and technological parameters of the soundboard for the implementation of a mathematical model of the technological process of the grain sorting machine. A kinematic study of the flat hinge mechanism of the deck of a grain sorting machine has been carried out, for which all geometric dimensions are known and the laws of motion of the leading link-the electric drive of the deck based on an asynchronous electric motor – have been determined. As a result, the following were determined: kinematic modes of deck vibrations under various technological conditions; the laws of motion of all links of the deck mechanism, displacement, speed and acceleration of the driven links; a mathematical model of the kinematic scheme of the deck of a grain sorting machine driven by an asynchronous electric motor has been developed.

Diseño y construcción de prototipo de máquina clasificadora de quinua por color usando sensores IR

La quinua (Chenopodium quinoa) es uno de los principales granos de especie domesticada oriunda de los Andes altiplánicos y valles altoandinos. Debido a que sus cosechas no siempre presentan un color homogéneo, en algunas empresas vienen utilizando máquinas clasificadoras para quinua que son adaptadas de otros cereales, cuyo funcionamiento no garantiza la eficiencia deseada. El propósito del estudio fue diseñar y construir un prototipo de máquina que clasifique los granos de quinua según el color deseado. Las partes principales de la máquina incluyen el diseño de la estructura mecánica que comprende tres zonas: alimentación, transporte y proceso y almacenamiento. Las piezas fueron construidas con materiales de acero y algunas piezas con filamento PLA fabricadas con impresora 3D; sistema de control electrónico, utilizando un microcontrolador para la adaptación de la señal de sensor IR y controlar los electroactuadores para la selección de quinuas homogéneas según los parámetros de colores permitidos; y control de funcionamiento mecánico, utilizando dos motores, una para la dosificación de los granos de quinua y otra para el movimiento de la faja transportadora. Para conseguir el mejor rendimiento se realizaron ajustes respecto a la velocidad y cantidad de quinuas, estableciendo a 24 hileras con una elevación de 22 grados respecto al eje horizontal, habiéndose conseguido la meta de 5 kg por hora y obtenido la homogeneidad de los granos de quinua con alta eficiencia y de mejor calidad para su comercialización.

Slow Civic Violence and the Removal of USPS Mail Sorting Machines During the 2020 Election

This article combines historical research with demographic analysis and neoliberal/rhetorical critique to put forth the concept of slow civic violence—indirect injuries on civic process, particularly within marginalized communities. The author ties the United States Postal Service's (USPS) rationale for removing mail sorting machines during the 2020 election year to systemic moves that damage democratic participation. The author conducts an empirical analysis of where the USPS mail sorting machines were removed to show how neoliberal arguments in favor of cost cutting make voting by mail a more precarious and uncertain act primarily for those who reside in communities of color.

Sensor-based and Robot Sorting Processes and their Role in Achieving European Recycling Goals - A Review

A circular economy is the stated aim of current technological and political developments in the waste management sector. Achieving the goal of a circular economy requires significant improvements in waste treatment technologies. For this reason, this paper summarises the relevant technologies, detailing the developments in the significant sensor-based sorting technologies. This review analyses the key spectral analysis methods like Near-Infrared Spectroscopy, Visual Spectroscopy, X-ray transmission, X-ray fluorescence analysis and Laser-Induced Breakdown Spectroscopy. This study further contains a detailed analysis of the standard sensor-based sorting construction types chute sorter, belt sorter and robot-aided sorting. Further insights in the branch of sensor-based sorting are permitted by describing the key players and stakeholders in sensor-based sorting, detailing the area of expertise and current fields of study for primary sensor and sorting machine suppliers. A convenient lookup table detailing the capabilities of these significant suppliers is provided. The last chapter summarises relevant trends and developments in digitalisation and Industry 4.0 in the waste and recycling sector, elaborating on relevant technology like digital waste management, sorting robots in waste management, smart villages and recyclable materials scanners. The reviewed data portrays the waste management industry’s substantial developments. While new technologies, like machine learning, convolutional neural networks and robot sorting, are increasingly implemented, a substantial discrepancy exists between technological capabilities and the current State-of-the-Art.

Sensor-based and Robot Sorting Processes and their Role in Achieving European Recycling Goals - A Review

A circular economy is the stated aim of current technological and political developments in the waste management sector. Achieving the goal of a circular economy requires significant improvements in waste treatment technologies. For this reason, this paper summarises the relevant technologies, detailing the developments in the significant sensor-based sorting technologies. This review analyses the key spectral analysis methods like Near-Infrared Spectroscopy, Visual Spectroscopy, X-ray transmission, X-ray fluorescence analysis and Laser-Induced Breakdown Spectroscopy. This study further contains a detailed analysis of the standard sensor-based sorting construction types chute sorter, belt sorter and robot-aided sorting. Further insights in the branch of sensor-based sorting are permitted by describing the key players and stakeholders in sensor-based sorting, detailing the area of expertise and current fields of study for primary sensor and sorting machine suppliers. A convenient lookup table detailing the capabilities of these significant suppliers is provided. The last chapter summarises relevant trends and developments in digitalisation and Industry 4.0 in the waste and recycling sector, elaborating on relevant technology like digital waste management, sorting robots in waste management, smart villages and recyclable materials scanners. The reviewed data portrays the waste management industry’s substantial developments. While new technologies, like machine learning, convolutional neural networks and robot sorting, are increasingly implemented, a substantial discrepancy exists between technological capabilities and the current State-of-the-Art.

Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network

In order to realize on-line detection of defective apples on a two-lane fruit sorting machine, an inspection module was constructed using NIR cameras and a diffuse illumination chamber. A real-time apple defects inspection method was proposed based on YOLO V4 deep learning algorithm. The input images were generated by combining NIR images in three consecutive rubber roller stations. Channel pruning and layer pruning methods were used to simplify the YOLO V4 network and accelerate the detection speed. A non-maximum suppression (NMS) method based on L1 norm is proposed to remove redundant prediction box after fine-tuning the pruned network. The test results indicated that the model size and inference time of the pruning-based YOLO V4 network was decreased by 241.24 megabyte (MB) and 10.82 ms, respectively, and the mean average precision (mAP) was increased from 91.82% to 93.74%, compared with the YOLO V4 network before pruning. The pruning-based YOLO V4 network based on NIR images was not affected by the variation of skin color and suitable for detects identification of different cultivars including ‘Fuji’ apple covered in red-yellow striping and red blush, ‘Golden Delicious’, and ‘Granny Smith’, with the average detection accuracy of 93.9% at the on-line test assessing five fruit per second. The overall results showed that the proposed pruning-based YOLO V4 network combined with the developed inspection module, had great potential to be implemented in commercial fruit packing line for fruit defects identification.

Rice sort machine design to improve rice granules quality

To produce quality rice, improvement in rice production management must begin at the farmer level (on-farm) by using high-quality seeds and good plant maintenance. Good quality rice is rice grains that don't break or crumble. To urge good quality rice, rice producers usually sort it manually. This method leads to long work processes and low productivity. Efforts to beat this problem are by designing a rice sorting machine which will separate whole rice and broken rice so on lighten the workload, improve rice quality, and increase prices. The results of the study obtained that the look specifications of the rice sieving machine were a). The sieving capacity is 10 kg of rice, the most drive is an electrical motor of 0.25 HP, a V-belt transmission, and an angled iron frame. Machine dimensions: length 120 cm, width 70 cm and height 90 cm). Machine testing the standard of rice grains employing a sorting machine can improve the standard of rice grains by reducing the presence of broken rice (size p=0.30 mm and below) a mean of 0.2 kg for 10 kg of sorted rice or increasing the standard by 1.91%. Machine testing from the ergonomic aspect.

Sorting Machines: Digital Technology and Categorical Inequality in Education

Existing scholarship suggests that schools do the work of social stratification by functioning as “sorting machines,” or institutions that determine which populations of students are provided educational resources needed to help them get ahead. We build on this theory of social reproduction by extending it to better understand how digital technology use is implicated in this process of unequal resource allocation in schools. We contend that educational resources, like digital technologies, are also sorted by schools. Drawing on scholarship from both education research and science and technology studies, we show how educational institutions have long played a role in constructing the value of technologies to different ends, by constructing hierarchies of technological activity, like “vocational” and “academic” computer use, even when strikingly similar. We then apply this lens to three areas of inquiry in education research: the use of digital technologies for instruction, school use of student data, and college admissions. Each illustrates how education scholars can view technologies as part of school sorting processes and with implications for inequality within and beyond the classroom.

100% visual inspection of tablets produced with continuous direct compression and coating

Visual appearance of tablets is an important property for patients. Since the visual appearance is most strongly influenced by the applied coating, this necessitates a high level of process control and homogeneity in the coating process. In recent years, a number of tablet coaters have been developed that can be used in combination with continuous tablet production lines. In this study, 180 kg of tablets were produced using a continuous direct compaction line with a throughput of 25 kg/h. Tablets were consequently subdivided into 12 lots and coated in a semi-batch drum coater directly after compression. For a detailed understanding of intra-lot and lot-to-lot variability, a 100% visual inspection of the tablets was performed using an automatic tablet inspection and sorting machine. All tablets were analyzed from all 6 sides and the unsuitable tablets were sorted out. In the worst lot, only 1 out of around 300 tablets was sorted out due to color mismatch. For some tablets, edge chipping was also observed, which would presumably not be detected during routine sampling. Root causes for the defects could be found in the intentionally chosen set of old punches and in the operation parameters of the coater. Nonetheless, the lot-to-lot variability according to all criteria was very low.

Development and Preliminary Evaluation of a New Apple Harvest Assist and In-Field Sorting Machine

HighlightsA new, improved apple harvest assist and in-field sorting (HAIS) machine was developed.The HAIS machine has several major innovations for enhanced harvesting and infield sorting efficiencies.Laboratory tests showed superior performance in fruit grading and sorting and bin filling and handling.Field tests showed only 0.4% of harvested fruit downgraded due to harvest-induced bruising.Abstract. Apples are still manually harvested in the U.S. and other countries, which contributes to high labor and production costs. Currently, no presorting is performed at harvest or in the field, which could incur significant postharvest costs or even economic loss to growers, when a high percentage of culls are present in harvested apples. Hence, technologies that integrate harvest assist and in-field sorting (HAIS) functionalities would be greatly beneficial to the apple industry by enhancing both harvest efficiency and postharvest cost savings. A new apple HAIS prototype machine was recently developed, which includes adjustable picking platforms with fruit receiving conveyors covering the entire height of trees, a computer vision-based grading and sorting system that is capable of sorting apples for color and size at a speed of up to 12 apples s-1, an automatic bin filling system for discharging graded fruit to bins, and a computer-controlled hydraulic system for automatic handling of empty and full bins. Inhouse developed operating software is fully integrated with the HAIS machine for automatic control of fruit grading and sorting and bin filling and handling. Laboratory tests showed superior performance of the machine in fruit grading and sorting and bin filling and handling. Field tests and demonstration (https://www.youtube.com/watch?v=21SB9FJjf2M) conducted in a commercial apple orchard in 2019 harvest season, showed that only 0.4% of harvested fruit were downgraded due to bruising, which exceeded the industry’s requirement for harvest-induced apple bruising. With further improvements, the HAIS machine can be commercially used for harvesting and infield presorting of apples. Keywords: Apple, Bin filling, Bruise evaluation, Bin handling, Computer vision, Harvest, In-field sorting.

Peaberry and normal coffee bean classification using CNN, SVM, and KNN: Their implementation in and the limitations of Raspberry Pi 3

<abstract> <p>Peaberries are a special type of coffee bean with an oval shape. Peaberries are not considered defective, but separating peaberries is important to make the shapes of the remaining beans uniform for roasting evenly. The separation of peaberries and normal coffee beans increases the value of both peaberries and normal coffee beans in the market. However, it is difficult to sort peaberries from normal beans using existing commercial sorting machines because of their similarities. In previous studies, we have shown the availability of image processing and machine learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest-neighbors (KNNs), for the classification of peaberries and normal beans using a powerful desktop PC. As the next step, assuming the use of our system in the least developed countries, this study was performed to examine their implementation in and the limitations of Raspberry Pi 3. To improve the performance, we modified the CNN architecture from our previous studies. As a result, we found that the CNN model outperformed both linear SVM and KNN on the use of Raspberry Pi 3. For instance, the trained CNN could classify approximately 13.77 coffee bean images per second with 98.19% accuracy of the classification with 64×64 pixel color images on Raspberry Pi 3. There were limitations of Raspberry Pi 3 for linear SVM and KNN on the use of large image sizes because of the system's small RAM size. Generally, the linear SVM and KNN were faster than the CNN with small image sizes, but we could not obtain better results with both the linear SVM and KNN than the CNN in terms of the classification accuracy. Our results suggest that the combination of the CNN and Raspberry Pi 3 holds the promise of inexpensive peaberries and a normal bean sorting system for the least developed countries.</p> </abstract>

The Role of Mill Machinery in Reducing DON-Toxin

In recent years the fusarium infection and accompanying DON–toxin contamination caused serious losses in the wheat growing and processing. Besides decrease of average yield it was serious problem that not a small part of the growned and harvested crop was unsuitable for food and forage production or the usage was limited. This phenomena caused problem in many countries all over the world according to data of scientific literature. There is a statement in the literature also that DON-toxin content can be decreased efficiently and safely only during the cultivation of plant and the possibilities for that are very limited during processing. We wanted to prove with our experiment that it is possible to decrease DON-toxin level of wheat items by application modern equipment and machineries, wherewith the losses and the food safety risks can be minimized. During our experiment we studied the effect of two milling machine on DON toxin content. (Sortex Z color sorter and Scule Verticone intensive surface cleaner). Both modern machine can be built-in the cleaning process in the mills. In our investigation we took samples from harvested wheat items during processing in the year of the experiment. The data were subjected to complex investigation and we evaluated them with different statistical methods. On this basis we can unequivocally state both machines are effective in reducing toxin level of basic material. In the year of the experiment the regression analysis showed that the Schule surface cleaner was more effective than the color sorting machine.

Qualitative analysis of post-consumer and post-industrial waste via near-infrared, visual and induction identification with experimental sensor-based sorting setup

Sensor-based sorting in waste management is a method to separate valuable material or contaminants from a waste stream. Depending on the separation property different types of sensors are used. Separation properties and their corresponding sensors are e.g. molecular composition with near-infrared sensors, colour with visual spectroscopy or colour line scan cameras, or electric conductivity with electromagnetic sensors.The methods described in this paper deal with the development of sorting models for a specific near-infrared, a visual spectroscopy and an induction sensor. For near-infrared and visual spectroscopy software is required to create sorting models, while for induction only machine settings have to be adjusted and optimized for a specific sorting task. These sensors are installed in the experimental sensor-based sorting setup at the Chair of Waste Processing Technology and Waste Management located at the Montanuniversitaet Leoben. This sorting stand is a special designed machine for the university to make experiments on sensor-based sorting in lab scale. It can be used for a variety of waste streams depending on the grain size and the pre-conditioning for the sensor-based sorting machine. In detail the methods to create these sorting models are described and validated with plastic, glass and metal waste.•Near-infrared spectroscopy measures the molecular composition of near-infrared-active particles.•Visual spectroscopy measures the absorption of visible light by chemical compounds.•Induction sensors use induced currents to detect nearby metal objects.