Color Sorter Research Articles

A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems

High-quality seeds improve the germination rate. Therefore, seed selection before sowing peanuts is crucial. Current peanut seed selection methods include color sorters and sieving machines, which are efficient but lack accuracy due to their reliance on a single indicator. Manual selection is inefficient and subject to subjective influences. Although machine vision and deep learning have performed well in crops such as corn and pepper, most existing research is based on PC or MATLAB platforms, which are not portable and are prone to interference, making them unsuitable for field applications. This study developed a lightweight model for recognizing peanut seed epidermal features. The model was based on deep learning and model quantization techniques. The transfer learning method was used to use four pre-trained models, EfficientNet_b0, EfficientNetv2-b0, MobileNet_v2_35_224, and NasNet_Mobile, as feature extraction layers, the input layer was added before the feature extraction layer, and the dropout and dense layers were added after the feature extraction layer to construct a classifier. The peanut seed selection network(PSSNet) models were constructed and named PSSNet-E, PSSNet-E2, PSSNet-M, and PSSNet-N, respectively, and trained on the Huayu 22 peanut seed dataset constructed in this study. The constructed models were compressed using model quantization technology, and four quantized models were obtained, namely PSSNet-Ef, PSSNet-E2f, PSSNet-Mf, and PSSNet-Nf. Finally, PSSNet-Mf, which had the best model evaluation, was selected as the peanut selection model for this study and deployed on a prototype for testing. Compared with the unquantized model, the size of the quantized model was reduced by two-thirds and the running speed was increased by 37%. A total of 400 Huayu 22 peanut seeds were selected as test samples, and 5 selection tests were conducted.The results showed that the average accuracy was 95.3%, which met the requirements of peanut selection at the production site.

Introduction and application analysis of FIFO-related methods

In this paper, an improved method of using binary pointers is proposed based on the analysis of the basic principles and structure of FIFO. The paper also presents a high-speed data acquisition system based on STM32 with an asynchronous FIFO designed to solve the insufficient data acquisition speed and high-power consumption problems of fiber-optic grating sensors in the electrical industry. The use of binary pointers significantly improves the processing speed and reduces the power consumption of the FIFO, thus improving the overall efficiency.In addition, the paper introduces the use of a field-programmable gate array (FPGA) design as the control core of a color sorter system. This overcomes the drawbacks of processor-based multi-channel color sorters, including low delay accuracy, poor consistency, and persistent signal loss. The FPGA offers superior performance due to its parallel computing capabilities and flexibility in algorithm implementation, making it ideal for high-performance applications such as color sorting systems.The color sorter system designed using FPGA technology is capable of sorting different materials based on color, shape, and size. The system's superior performance is due to the use of an asynchronous FIFO buffer that enables real-time color recognition and processing. By incorporating the FIFO buffer, the system can achieve high processing speeds, consistent color recognition, and minimal signal loss, providing a valuable solution for applications requiring high-precision color sorting.

Review on the Beneficiation of Li, Be, Ta, Nb-Bearing Polymetallic Pegmatite Ores in China

Lithium-bearing polymetallic pegmatite ores are an important raw material for lithium extraction. They contain not only lithium but also other associated elements such as beryllium, tantalum, and niobium, with great recovery values. It is therefore often called lithium-bearing polymetallic pegmatite ore (LPPO). The recovery and utilization of Be-bearing minerals in LPPOs have yet to be further studied. This paper briefly expounds the geological aspects of LPPO deposits in China and Chinese experiences on the beneficiation of LPPOs, with special emphasis on the flotation separation of lithium-beryllium minerals. In LPPO, spodumene is the main target mineral for lithium, while beryl is the main Be-bearing mineral in a fine-grained embedded state. If the BeO grade of LPPO is greater than the industrial grade (BeO ≥ 0.04%), it will be processed for recovery. Tantalum and niobium minerals are mainly in the form of tantalite, columbite, or ferrotapiolite, which may be recovered by gravity separation or magnetic separation. Gangue minerals are mainly composed of albite and quartz. Currently, the most commonly used methods for separating the target minerals from gangue are dense medium separation and flotation. The manual sorting method has become obsolete and is expected to be replaced by machine sorting methods such as color sorters and X-ray transmission sorters. Flotation is the main method for the separation of fine-grained beryl and spodumene. The success of flotation depends on the selection of suitable pretreatment methods and appropriate flotation reagents for altering the surface properties of spodumene and beryl and for expanding the floatability differences between spodumene and beryl and between spodumene and gangue.

Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm

Color sorting is a technological operation performed with the aim of classifying compliant and noncompliant agricultural products in large-capacity industrial systems for agricultural product processing. This paper investigates the application of the YOLOv3 algorithm on raspberry images as a method developed for the detection, localization, and classification of objects based on convolutional neural networks (CNNs). To our knowledge, this is the first time a YOLO algorithm or CNN has been used with original images from the color sorter to focus on agricultural products. Results of the F1 measure were in the 92–97% range. Images in full resolution, 1024 × 1024, produced an average detection time of 0.37 s. The impact of the hyperparameters that define the YOLOv3 model as well as the impact of the application of the chosen augmentative methods on the model are evaluated. The successful classification of stalks, which is particularly challenging due to their shape, small dimensions, and variations, was achieved. The presented model demonstrates the ability to classify noncompliant products into four classes, some of which are appropriate for reprocessing. The software, including a graphic interface that enables the real-time testing of machine learning algorithm, is developed and presented.

Colour Detecting and Sorting Robot

Abstract: In this digital world, color processing in numerous industries gives us more leverage to unravel the consistency problem of continuous manual sorting. This document are a replacement approach to continuously detect and classify objects and keep them in a very specific place. Color processing attracts plenty of attention since the assistance of contemporary technology results in the likelihood of expanding the scope of its application in various areas. The Arduino Nano microcontroller, TCS3200 colour sensor, servo motor, and other electronic components are wont to study, develop, and build a color sorter. This work involves sensors that detect the colour of the item and send the signal to the Arduino. The microcontroller sends a proof to the circuitry that drives the varied motors to activate the thing and place it within the specified location. supported the detection, everything moves to the required position releases the thing, and returns to the first position. The system is in a position to quickly classify the article supported its color at the respective color station. Sorting objects is an essentially mechanical process that needs difficult work. The chronically manual layout ends up in consistency problems. Above all, machines can do boring tasks that humans are superior to. Worker burnout in sequential production structures can result in reduced execution and purpose issues in maintaining the article. Recognize the colour of the article but in no way a machine. during this article, a compact registration near the arrangement of things supported color was implemented using the TCS3200 color sensor with servo motors in conjunction with Arduino Nano.

At‐line monitoring of industrial corn seed classification quality by digital images analysis using smartphone

This study aimed to evaluate the feasibility of using the PhotoMetrix PRO® application to perform the selection of good quality corn seeds from defective ones, through digital images and the multivariate analysis of color elements. An optimization of the experimental conditions was carried out. Eighteen experiments were performed varying the size of the analysis recipient, the distance of the sample from the smartphone and the size of the region of interest (ROI). A calibration model was built using a partial least squares (PLS) regression model in the determined experimental conditions, and the prediction of external samples was then performed. The conditions that provided the smallest cross-validation error consisted of using the smaller chamber, the largest ROI, and the longest distance from the smartphone to the sample. The coefficient of determination obtained by the model was .976. In addition, the root-mean-square error of calibration (RMSEC) and the root-mean-square error of cross-validation (RMSECV) obtained were 1.2% and 4.8%, respectively. In the prediction, two outliers were observed, and the RMSEP dropped to 2.2% when they were removed. The proposed methodology shows great potential to contribute to the industrial process of corn seed production. It is fast, accurate, and low cost, allowing the analysis to be performed 15 times faster than the current process. Practical Applications Corn is one of the most important commodities in Brazil's economy. During the processing of corn seeds in the industry, those of good quality are selected among the others through parameters, such as color and density, for example. The gravity table and the Color Sorter machine are equipment used for this purpose. However, even if the machine's separation is effective, an important percentage of good seeds are wasted. To reduce the waste of good seeds remaining in the tailings of these machines, the operators carry out a manual inspection of tailings samples, determining the quantity of quality seeds present there for later adjustment of the machines. This visual and manual analysis becomes time-consuming, subjective, and imprecise. Thus, as an alternative, this study proposes the use of digital image and partial least square model built by a mobile app to determine the quantity of quality corn seeds in the machines' wastes.

A Review on Arduino Based Color Sorting Machine

Abstract: In this digital world, color processing in different industries gives us more leverage to solve the consistency problem of continuous manual sorting. This document will be a new approach to continuously detect and classify objects and keep them in a specific place. Color processing attracts a lot of attention since the help of modern technology leads to the possibility of expanding the scope of its application in various areas. The Arduino Nano microcontroller, TCS3200 colour sensor, servo motor, and other electronic components are used to study, develop, and build a colour sorter. This work involves sensors that detect the color of the object and send the signal to the Arduino. The microcontroller sends a signal to the circuitry that drives the various motors to activate the object and place it in the specified location. Based on the detection, everything moves to the specified position releases the object, and returns to the original position. The system is able to quickly classify the object based on its color at the respective color station. Sorting objects is an essentially mechanical process that requires difficult work. The chronically manual layout leads to consistency problems. Above all, machines can do boring tasks that humans are superior to. Worker burnout in sequential production structures can lead to reduced execution and purpose issues in maintaining the object. Recognize the color of the article but in no way a machine. In this article, a compact registration near the arrangement of items based on color was implemented using the TCS3200 color sensor with servo motors in conjunction with Arduino Nano. Keywords: Automation TCS3200, Arduino Nano, Servo Motor.

Colour discernment of tomatoes using machine vision system with OpenCV Python and Raspberry Pi

Colour is the first quality attribute of food that consumers examine and it is an important component of food quality that influences market acceptance. Classification of fruits by visual inspection is an arduous, time-consuming process and prone to human error. The machine vision system is a distributed control system that integrates several machine vision modules with a control module and a user interface unit. This research proposes a method for recognising and sorting tomato fruits into a preferred location continuously. Before designing colour sorter the physical properties of fruits were studied. The major and minor diameter of the tomatoes ranges from 45-60mm and 35-50mm, respectively. The mean geometric diameter, sphericity and surface area were 48.64mm,0.94 and 6477.14mm2 respectively. The average length, width, thickness, bulk density and true density were 54.63mm, 48.44mm, 51.42mm, 0.6874g/cm3 , 0.9852g/cm3 , respectively. The colour sorter was researched, designed and created with Raspberry Pi, USB camera, servo motor and different digital as well as mechanical components. The model used for Raspberry Pi is Raspberry Pi 3 Model B+, USB camera with a video resolution of 640 x 480, 4.2-6V servo motor. Image evaluation is completed on each captured picture and Raspberry Pi will do the selection of which fruit can be sorted. Specific programming code in Python is written for this system. The developed colour sorter captures images and diverts fruits into the respective channel at the rate of 1800 fruits/h (i.e.one fruit per 2 seconds).

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.

U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process

Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign object detection algorithm is an effective method to overcome the disadvantages of conventional foreign object detection methods mainly used in food inspection. For example, color sorter machines cannot detect foreign objects with a color similar to food, and the performance is easily degraded by changes in illuminance. Therefore, to detect foreign objects, we use a deep learning-based foreign object detection algorithm (model). In this paper, we present a synthetic method to efficiently acquire a training dataset of deep learning that can be used for food quality evaluation and food manufacturing processes. Moreover, we perform data augmentation using color jitter on a synthetic dataset and show that this approach significantly improves the illumination invariance features of the model trained on synthetic datasets. The F1-score of the model that trained the synthetic dataset of almonds at 360 lux illumination intensity achieved a performance of 0.82, similar to the F1-score of the model that trained the real dataset. Moreover, the F1-score of the model trained with the real dataset combined with the synthetic dataset achieved better performance than the model trained with the real dataset in the change of illumination. In addition, compared with the traditional method of using color sorter machines to detect foreign objects, the model trained on the synthetic dataset has obvious advantages in accuracy and efficiency. These results indicate that the synthetic dataset not only competes with the real dataset, but they also complement each other.

Development of a farmer‐friendly portable color sorter cum grader for tomatoes

AbstractTomato (Solanum lycopersicum) is a major horticulture crop grown worldwide. The ripened tomatoes are excellent source of antioxidants. On the other hand, the transportation of ripened tomatoes to long distances is a challenging task. However, the unripe tomatoes are opted for long‐distance transportation because of their extended shelf‐life. A sensor was developed with an efficient color sorting program using integrated circuits along with a divergent rolling grader. The developed color sorter cum size grader works on the principle of energetic reflection based on measuring the intensity of light and it comprises feed hopper, chain conveyor, belt conveyor, three color sensor (TCS3200), collecting ducts, inlet hopper, grader, outlet section, and collecting trays. Developed machine was operated by a single‐phase 2 hp motor. The performance of the developed machine was analyzed by measuring capacity, sorting efficiency, overall grading efficiency, and skin damage. Based on the performance evaluation, the maximum sorting efficiency and overall grading efficiency obtained were 94.5% and 94.1, 94.1, and 94.6% for ripe and unripe fruits, respectively.Practical ApplicationsTomato is one of the important commodities, fetching highly fluctuating market values. Color sorting and size grading are very much needed at the field level to overcome human drudgery and to enhance the market value. In this context, a portable color sensor cum grader has been developed, which can be used by the farmers/traders to get higher prices in the market. The capacity of newly developed equipment was approximately 40 kg/hr. The cost of operation of color sorting cum grading equipment was Rs. 3/kg of tomatoes.

Rapid Softness Prediction and Microbial Spoilage Visualization of Whole Tomatoes by Using Hyper/Multispectral Imaging

The choice of selecting fruit for canned whole tomatoes is driven by several quality attributes including sweetness, acidity, and softness of tomatoes. Moreover, tomatoes can be contaminated with a variety of molds during cultivation, harvest, and transportation. Conventional evaluation operations for tomato softness and microbial spoilage are usually time-consuming, destructive, and high-cost. One strategy for rapid tomato sorting is to utilize hyper/multispectral imaging. This paper proposes to improve on traditional broad-band infrared imaging of existing color and dirt sorters by increasing the spectral resolution of the information collected. The findings of this study will characterize the potential of the technology in terms of predicting tomato softness and identification of tomato microbial spoilage for further development by the industry.

RESEARCH OF DESIGN AND TECHNOLOGICAL PARAMETERS OF CLEANING CLOVER SEEDS BY COLOR SORTING

The article presents the results of study to establish the design and technological parameters of an experimental sample of the color sorter, carried out within the framework of the grant project of the Ministry of Education and Science of the Republic of Kazakhstan AP05130398. The regularities of the technological process of color sorting - cleaning of clover seeds from seeds of quarantine and hardseparable impurities and rational regimes providing high efficiency of processing of clover seed material - have been established. A constructive feature of the tray feeding the seed mixture is proposed, which allows reducing the massiveness of the separator while increasing the productivity, reducing the useful production area.

Improvement of Rice Quality Using Grain Color Sorter During Early Transplanting Cultivation in the Southern Plain of Korea

Improvement of Rice Quality Using Grain Color Sorter During Early Transplanting Cultivation in the Southern Plain of Korea

Near Field Differential Interference Contrast Microscopy

Near field scanning optical microscopy exploiting differential interference contrast enhancement is demonstrated. Beam splitting in the near field region is implemented using a dual color probe based on plasmonic color sorter idea. This provides the ability to illuminate two neighboring points on the sample simultaneously. It is shown that by modulating the two wavelengths employed in exciting such a probe, phase difference information can be retrieved through measuring the near field photoinduced force at the difference of the two modulation frequencies. This difference in frequency is engineered to correspond to the first resonant frequency of the cantilever, resulting in improved SNR, and sensitivity. The effect of both topographical and material changes in the proposed near field differential interference (NFDIC) technique are investigated for CNT and silica samples. This method is a promising technique for high contrast and high spatial resolution microscopy.

Effect of thickness and maturity on protein content of Japonica brown rice collected during postharvest processing

The rice industry requires new technologies to increase production in response to world population growth. These include technologies that ensure rice quality in postharvest processing. Protein content defines rice quality and palatability. Differences in kernel thickness and maturity affect the protein level of harvested rice. We therefore analysed the relationship between kernel thickness and maturity on protein content of Japonica brown rice collected during postharvest processing. We fractionated the rice into six thickness fractions and classified each fraction into mature, immature and chalky levels of maturity. Results showed that thickness fraction and maturity level affected protein content. Each increase in thickness produced an increase in the percentage of sound whole kernel and a decrease in protein content. The mature sample had the lowest protein content among maturity levels within each thickness fraction. Based on sound whole kernel and protein content values, we classified the mature sample in higher thickness fractions as having high-quality appearance and palatability. There were significant differences in protein content between different thickness fractions of the mature sample suggested that we can sort rice protein by thickness fraction. Moreover, differences in protein content suggested that we can use the thickness grader and colour sorter machines to sort rice into high or low protein content fractions. This information can contribute to more accurate quality constituent determination through the development of a rice protein sorter. This would support the production of high-quality rice and help address the demand for more efficient technology for rice postharvest processing.

Microbial loads on selected fresh blueberry packing lines

Foodborne outbreaks have been linked to the consumption of blueberries contaminated with pathogenic bacteria. The microbial quality of blueberries could be influenced by the conditions of hygiene prevailing during post harvest handling. This study evaluated the hygienic conditions of 6 selected fresh blueberry packing lines. Five of the packing lines were sampled twice and the remaining one was sampled once during the summer of 2015 and the summer of 2017. A delimited area (100 cm2) on each of the ten selected sites of the packing lines was swabbed with sterile sponges before the packing started (AM samples), during lunchtime break (NOON samples), and at the end of a packing day (PM samples). The sponges were thoroughly rinsed with Dey-Engley neutralization broth which was subsequently sampled for total aerobes, yeasts and molds, total coliforms, fecal coliforms, and enterococci. Results showed that sample site and sampling time had a significant influence (P ≤ 0.05) on total aerobic, yeast and mold, and total coliform counts. The PM samples had significantly higher (P ≤ 0.05) total aerobic and yeast and mold counts than the NOON samples which had significantly higher (P ≤ 0.05) counts than the AM samples. Fourty-six out of the 310 (14.8%) collected samples tested positive for enterococci while 27 (8.7%) samples tested positive for fecal coliforms. Berry lugs, rubber belts on color sorters, and premature berry disposing areas had significantly higher (P ≤ 0.05) microbial counts than the other sites. The study suggests that some sites along fresh blueberry packing lines could become contaminated by microorganisms during packing. Whether these contaminated sites will become a food safety concern depends on the incidence of pathogen presence in the microbial community and efficacy of routine sanitizing treatments.

Manipulating Light in Coupled Asymmetric Nanostructures Induced by a Visible–NIR Laser

We design an asymmetric nanostructure in the longitudinal direction at the visible–NIR range, which enables high enhancement factor and has the properties of Fano resonance induced by a visible–NIR laser. By simulating and analyzing the resonance frequency spectra of various nanorods, nanodipoles, and combined nanoantennas, we optimize the resonant spectra and enhanced factor of such nanoantennas. It has broad-band resonant spectra with a FWHM from 800 to 1,100 nm and possesses two resonant peaks at 870 and 1,000 nm, with an enhancement factor of 24. The current density distribution in such nanoantennas with different phases is also simulated in order to investigate its resonant mode. This theoretical study paves the way towards nanoscale lightwave control and spectral splitting. The designed nanodevices provide great potential for applications in ultrasensitive color sorters and biosensors induced by visible–NIR lasers.

Color Separation through Spectrally-Selective Optical Funneling

We present the concept and a basic design for a free-space color sorter, which can efficiently separate different spectral components of an optical beam. The structure is based on a resonant cavity formed between a metallic wall and a stack of partially reflecting screens. Such a cavity supports multiple leaky-wave resonances, which enable spectrally selective funneling of the incident beam through apertures in the metallic wall. The apertures are coupled to resonators with different resonance frequencies that allow separating different spectral components of the beam. The proposed concept can potentially overcome the efficiency limitations of color filter arrays, with important consequences in the design of fast and efficient spectral imaging systems.

Hybridization between nanocavities for a polarimetric color sorter at the sub-micron scale.

Metallic hole arrays have been recently used for color generation and filtering due to their reliability and color tunability. However, color generation is still limited to several microns. Understanding the interaction between the individual elements of the whole nanostructure may push the resolution to the sub-micron level. Herein, we study the hybridization between silver nanocavities in order to obtain active color generation at the micron scale. To do so, we use five identical triangular cavities which are separated by hundreds of nanometers from each other. By tuning either the distance between the cavities or the optical polarization state of the incoming field, the transmitted light through the cavities is actively enhanced at specific frequencies. Consequently, a rainbow of colors is observed from a sub-micron scale unit. The reason for this is that the metallic surface plays a vital role in the hybridization between the cavities and contributes to higher frequency modes. Cathodoluminescence measurements have confirmed this assumption and have revealed that these five triangular cavities act as a unified entity surrounded by the propagated surface plasmons. In such plasmonic structures, multi-color tuning can be accomplished and may open the possibility to improve color generation and high-quality pixel fabrication.