Send Message

CHNSpec Technology (Zhejiang)Co.,Ltd chnspec@colorspec.cn 86--13732210605

About Us
Why Choose Us
CHNSpec Technology (Zhejiang)Co.,Ltd was found in 2008, and we are specialize in the R&D, production and sales of colorimeters.
View More
CHNSpec Technology (Zhejiang)Co.,Ltd

HIGH QUALITY

Trust Seal, Credit Check, RoSH and Supplier Capability Assessment. company has strictly quality control system and professional test lab.
CHNSpec Technology (Zhejiang)Co.,Ltd

DEVELOPMENT

Internal professional design team and advanced machinery workshop. We can cooperate to develop the products you need.
CHNSpec Technology (Zhejiang)Co.,Ltd

SOURCE FACTORY

Advanced automatic machines, strictly process control system. We can manufacture all the Electrical terminals beyond your demand.
CHNSpec Technology (Zhejiang)Co.,Ltd

INTIMATE SERVICE

Bulk and customized small packaging, FOB, CIF, DDU and DDP. Let us help you find the best solution for all your concerns.

2013

Year Established

200+

Employees

100000+

Customers Served

30000000+

Annual Sales

Our Products

Featured Products

China CHNSpec Technology (Zhejiang)Co.,Ltd
Contact Us
Play video
Contact at Any Time
Send

CHNSpec Technology (Zhejiang)Co.,Ltd

Address: No. 166 of Wenyuan Road,Jianggan District,Hangzhou City, Zhejiang Province, China
Fax: 86--13732210605
Phone: 86--13732210605
Our Products
Top Products
Our Cases
Recent Industrial Projects
Lastest company cases about Three methods for color measurement
2020/04/01
Three methods for color measurement
Color measurement is mainly divided into the measurement of the color of the light source and the measurement of the color of the object. The object color measurement is divided into fluorescent object measurement and non-fluorescent object measurement. In actual production and daily life, color measurement of non-fluorescent objects is widely used. It is mainly divided into two categories: visual color measurement and instrument color measurement. Among them, instrument color measurement includes photoelectric integration method and spectrophotometry method.   1. Visual method The visual method is the visual perception of light produced by the eyes, the brain, and our life experience. The light we see with the naked eye is generated by electromagnetic waves with a narrow wavelength range, and electromagnetic waves of different wavelengths show different colors The recognition of color is the visual nerve sensation caused by the naked eye after being stimulated by electromagnetic wave radiation energy. The unknown colors of the individual components are added together to describe the resulting unknown colors. Although it is most suitable for color evaluation. The way to rely on it is with the help of the human eye, and it is simple and flexible, but due to the experience of observers and psychological and physiological factors The impact of this method makes the method too many variables and cannot be described quantitatively, which affects the accuracy of the evaluation.   2.The photoelectric integration method For a long time, the density method has occupied a very high position in color measurement, but with the application of CIE1976L *, a *, b * gradually becoming widespread, and has covered the entire work flow from press to printing, people are more and more aware of color The importance of degree, and the rapid development of modern colorimetric have also laid the foundation for the objective evaluation of color by photoelectric integration instruments ( precision color difference meters). The photoelectric integration method is a common method used in instrument color measurement in the 1960s. It does not measure the color stimulus value of a certain wavelength, but measures the tristimulus values X, Y, and Z of the sample through integral measurement over the entire measurement wavelength interval, and then calculates the chromaticity coordinates and other parameters of the sample. When using such three photo detectors to receive light stimuli, the tristimulus values X, Y, and Z of the sample can be measured with one integration. The filter must meet Luther's conditions to accurately match the photo detector. The photoelectric integration instrument cannot accurately measure the tristimulus value and chromaticity coordinates of the excellent source, but can accurately measure the color difference between the two color sources, so it is also called a color difference meter. Foreign color difference meters have been mass-produced since the 1960s, and China has been developing such instruments since the early 1980s. Nowadays, the CS-210 precsision colorimeter produced by Hangzhou CHNSpec Technology Co.,Ltd has been used. CS-210 Precision Colorimeter   3. Spectrophotometry Spectrophotometry is also called spectrophotometer. It compares the light energy reflected (transmitted) by the sample with the standard reflected (transmitted) light energy under the same conditions to obtain the spectral reflectance of the sample at each wavelength, and then uses CIE The provided standard observer and standard light source are calculated according to the following formula to obtain the tristimulus values X, Y, and Z, and then X, Y, and Z are used to calculate the chromaticity coordinates x according to the formulas such as CIE Yxy and CIE Lab. y, CIELAB chromaticity parameters, etc. The spectrophotometer determines the color parameters by detecting the spectral components of the sample. It can not only give the absolute values of X, Y, Z and the color difference value △ E, but also give the spectral reflectance value of the object, and can draw the object. Therefore, it is widely used in color matching and color analysis. The use of such instruments can achieve high-accuracy color measurement, calibration of photoelectric integral color measurement instruments, and establishment of chromaticity standards. Such instruments were first developed in China. CS-600 Integrating Sphere Color Spectrophotometer is color spectrum. Therefore, the spectrophotometer is an authoritative instrument in color measurement.   Color Spectrophotometer CS-600   Company introduction Our CHNSpec Technology Co., Ltd are specialized on manufacturing haze meter, spectrophotometers, colorimeters and gloss meters. Our products have gotten 10 Invention Patents including 1 American Invention Patent, 8 Utility Model Patents, 4 Appearance Patents and 3 Software Copyrights till now.    
Lastest company cases about Objective Measurement of Transparency
2020/03/26
Objective Measurement of Transparency
Measurement and analysis of haze and clarity guarantee a uniform and consistent product quality and help analyze influencing process parameters and material properties, e.g.cooling rate or compatibility of raw materials.   The figure on the picture shows the measurement principle of the haze meter:   A light beam strikes the specimen and enters an integrating sphere. The sphere's interior surface is coated uniformly with a matte white material to allow diffusion. A detector in the sphere measures total transmittance and transmission haze. A ring sensor mounted at the exit port of the sphere detects narrow angle scattered light ( clarity). Standard Methods The measurement of Total Transmittance and Transmission Haze is described in international standards. Two different test methods are specified: 1. IS013468 Compensation method 2. ASTM D1003 Non-compensation method The compensation method takes the light reflected on the sample surface into account. Differences between the two methods can be approximately 2 Total Transmittance on clear, glossy samples.   ASTM D 1003 Measurement conditions are different during calibration and actual measurement. During calibration, part of the light escapes through the open entrance port of the haze meter. While taking a measurement, the entrance port is covered with the sample, thus, the amount of light in the sphere is increased by the light reflected at the sample surface.     ISO13468 Measurement conditions are kept equal during calibration and measurement due to an additional opening in the sphere. During calibration the sample is placed at the compensation port. For the actual measurement, the sample is changed to the entrance port. Thus, the so-called sphere efficiency is independent of the reflection properties of the sample.     Two Standard Methods in one Unit The clarity and haze meter CS-720 complies with both ASTM and ISO measurement standards. It can meet the following measurement standards ASTM D1003 / D1044, ISO13468 / ISO14782, JIS K7105, JIS K7361, JIS K7163 and other international standards. If any inquiry, you are welcome to contact us.  
Lastest company cases about Factors affecting haze measurement
2020/03/25
Factors affecting haze measurement
What is haze? Haze is also called turbidity. It indicates the degree of unclearness of transparent or translucent materials. It is the appearance of cloudiness or turbidity caused by light scattering inside or on the surface of the material. It is expressed as the percentage of the ratio of the scattered light flux to the light flux through the material.   Why measure haze? Haze measurement can be used to quantify the optical properties of plastics and packaging films. Obscure films in packaging applications can reduce consumer perception of quality, such as when packaging products look blurry. For plastics with haze, the visibility of the test material becomes more pronounced and reduces the contrast of the observed objects.   Factors affecting haze measurement Part1: light source Different light sources have different relative spectral energy distributions. Because various transparent plastics have their own spectral selectivity, the same material is measured with different light sources, and the obtained light transmittance and haze value are different. The darker the color, the greater the impact.In order to eliminate the influence of the light source, the International Institute of Illumination (CIE) has specified three standard light sources A, B, and C. This method uses a "C" light source.       Part2: Influence of surface condition The surface state of the sample mainly refers to whether the surface is flat and smooth, whether there are scratches and defects, and whether it is contaminated.       Part3: Effect of specimen thickness As the thickness of the sample increases, the light absorption increases, the light transmittance decreases, and light scattering increases, so the haze increases. Transmission and haze can only be compared at the same thickness.  
Lastest company cases about How to calculate haze of transparent acrylic plastic sheet?
2020/03/14
How to calculate haze of transparent acrylic plastic sheet?
What is acrylic sheet? Acrylic is also called special-processed plexiglass. It is a replacement product of plexiglass. The light box made of acrylic has good light transmission, pure colors, rich colors, beautiful and flat, taking into account the two effects of day and night, long life, does not affect the use, and other features.   How to calculate transmittance? In the process of measuring the haze and light transmittance of the sample, it is necessary to measure the incident light flux (T1), the transmitted light flux (T2), the scattered light flux (T3) of the instrument, and the scattered light flux (T4) of the sample. Calculation method of Transmittance: Tt= T2/ t1x100%   How to calculate haze? Haze: H= [t4-t3 (T2/T1)]/ t2x100% The formula of haze value H can be simplified as: H(%)= [(T4/T2)-(T3/T1)]×100%   How to Measure Acrylic Plastic Sheet?(The products that measure haze are Color Spectrum TH-100, CS-700, CS-701 and CS-720) Take Color Spectrum Haze Meter TH-100 as an example 1.Start Connect the instrument to the power source, press the power key, the indicator light is always blue, and the instrument starts normally. 2.0% and 100% calibration. Put the 0% calibration cover on the test port so that the integrating sphere does not receive any light. Press the OK key on the side of the instrument to calibrate.100%: Keep the test port open, let the light from the light source pass through the test port, and press the OK key on the side of the instrument for calibration. 3.Measure After calibration, place the transparent acrylic plastic sheet in the test port and click the test button next to the instrument. The result will be available in 2 seconds. The operation process is very simple.  
Lastest company cases about How to calculate haze
2020/03/09
How to calculate haze
Haze : Wide Angle Scattering   The light before passing through the sample is called incident light, the entire light after passing through the sample is called transmitted light, and the scattered light with a scattering angle greater than 2.5 ° after the transmission sample is called scattered light, haze Is the scattered light than the transmitted light (as show in green color of picture 2) and Tt is the total transmitted light (as show in pink color of picture 1).   So haze equation is Haze = Td / Tt.     Haze Measuring Instrument   We will introduce how to measure haze by CHNSpec Haze Meter TH-100. It can meet both ISO and ASTM standards.   TH-100 haze meter   What is the measurement method of TH-100? This is the light path structure diagram of this haze meter. The light source emits parallel light, passes through the sample and enters the integrating sphere. Part of the transmitted light is parallel light and part is scattered light. A photoelectric sensor is installed on the inner wall of the integrating sphere perpendicular to the parallel beam to obtain the light flux signal. The light trap is used to absorb all the incident light when there is no sample in the test port. The light trap is equipped with a shutter, which is coated with the same high reflectivity coating as the integrating sphere wall. The shutter can be opened and closed as required. Light trap: When measuring the haze, the light trap will open (because the scattered light will be collected to calculate the haze); when measuring the total transmittance, the light trap will be closed; haze meter TH-100 can be automatically measured, all you have to do is place the sample at the test.     For more details of haze meter TH-100, you can refer to the following url   1). Haze Meter TH-100 Working Video https://www.youtube.com/watch?v=qtyhHWB8r_Y&t=24s   2). TH-100 Haze Meter Accuracy Test Video https://www.youtube.com/watch?v=k3b4X-kERss&feature=youtu.be   CHNSpec Tech is specialized on provide color, gloss and haze measurement solutions. If any future inquiry, you are welcome to contact me for more details.
Event
Our Latest News
Lastest company news about Coal sample hyperspectral image acquisition and processing methods
Coal sample hyperspectral image acquisition and processing methods
In the research and production practice of coal industry, it is very important to obtain accurate information of various characteristics of coal for optimizing coal utilization and improving product quality. Hyperspectral image technology, as a powerful means of analysis, can provide abundant information of coal internal structure and composition, and its application is based on efficient and accurate coal sample hyperspectral image acquisition and processing methods. Hyperspectral imaging technology is an advanced technology integrating optics, electronics, computer science and other disciplines. Its working principle is based on the differences in the absorption, reflection and scattering properties of different substances to different wavelengths of light. Through hyperspectral imaging equipment, we can obtain the reflectance information of coal in the continuous spectral range, which is like the "fingerprint" of coal, containing rich material composition and structure information. Compared with traditional imaging technology, hyperspectral imaging technology has higher spectral resolution and can be accurate to the wavelength difference at the nanometer level, which can capture the spectral characteristics of various components in coal in more detail. In this paper, a 900-1700nm hyperspectral camera is used, and FS-15, a product of Color Spectrum Technology (Zhejiang) Co., LTD., can be used for related research. Short-wave near-infrared hyperspectral camera, the acquisition speed of the full spectrum up to 200FPS, is widely used in the composition identification, substance identification, machine vision, agricultural product quality, screen detection and other fields. The application of hyperspectral imaging technology in coal calorific value detection is relatively simple and efficient. First, hyperspectral image data is obtained by scanning coal samples with hyperspectral imaging equipment.The application of hyperspectral imaging technology in coal calorific value detection is relatively simple and efficient. First, hyperspectral image data is obtained by scanning coal samples with hyperspectral imaging equipment.   Hyperspectral image acquisition interface   These data contain information about the reflectivity of coal at different wavelengths. Then, professional data processing software is used to preprocess the acquired image data, remove noise, correct spectrum, etc., in order to improve the quality of data. (a) The original image                                 (b) Area of interest Selection of regions of interest in coal hyperspectral images   Region of interest mean spectral curve   Seven point SG smooth filtering   Due to the characteristics of the instrument itself and the influence of environmental factors, the collected spectrum may have some problems such as wavelength drift and intensity deviation. The purpose of spectral correction is to correct these deviations so that they can accurately reflect the real spectral characteristics of coal samples. Common spectral calibration methods include wavelength calibration and radiation calibration. Wavelength calibration Calibrates the wavelength accuracy of the imaging spectrometer by using standard materials with known spectral characteristics, such as mercury lamps and neon lamps, to ensure that the wavelength value corresponding to each pixel is accurate. Radiometric calibration is to convert the gray value of the image into the actual reflectance value by measuring the standard whiteboard with known reflectance, thus eliminating the influence of factors such as instrument response and uneven illumination on the spectral intensity. The results of multivariate scattering correction are shown in the figure. Multivariate scattering correction results   Standard normal transformation Standard normal transformation result   The acquisition and processing of hyperspectral images of coal samples is a complicated and critical process. By selecting suitable acquisition equipment, optimizing the acquisition process and using advanced image processing methods, abundant and accurate coal information can be extracted from hyperspectral images, which provides strong technical support for the research, production and quality control of coal industry. With the continuous development of technology, the application prospect of hyperspectral image technology in the coal field will be broader, and it is expected to bring new breakthroughs for the development of the coal industry.
Lastest company news about Quantitative detection of goose and duck mixed velvet by hyperspectral camera
Quantitative detection of goose and duck mixed velvet by hyperspectral camera
In the textile industry, goose down and duck down have become high-quality raw materials for making high-grade thermal products because of their excellent thermal properties. However, there is a big difference in the market price between goose down and duck down. Some bad merchants often mix duck down into goose down for the pursuit of high profits, which not only damages the interests of consumers, but also disrupts the market order. Therefore, accurate and efficient quantitative detection of goose and duck mixed velvet is particularly important. In recent years, the development of hyperspectral camera technology has provided an innovative solution to this detection challenge. 一、Sample preparation: Collect a large number of pure goose down and duck down samples to ensure that their sources are reliable and representative. Use high-precision electronic scales to accurately weigh goose down and duck down according to different proportions, and configure a series of goose and duck mixed velvet samples with known mixing proportions, such as setting 5%, 10%, 15%... Samples of different proportions such as 95% duck down were mixed, and multiple repeated samples were set for each proportion to improve the accuracy and reliability of the experiment. The configured mixed wool sample is evenly laid on the special sample table to ensure uniform sample distribution without overlap and vacancy, and to ensure that the hyperspectral camera can obtain comprehensive and accurate spectral information. 二、Hyperspectral image acquisition: This paper uses a 400-1000nm hyperspectral camera, which can be used for related research FS13, the product of Hangzhou Color Spectrum Technology Co., LTD. The spectral range is 400-1000nm, the wavelength resolution is better than 2.5nm, and up to 1200 spectral channels can be reached. The acquisition speed can reach 128FPS in the full spectrum, and the maximum after band selection is 3300Hz (support multi-region band selection). Each mixed wool sample is photographed multiple times to obtain images from different angles to reduce detection errors caused by local feature differences of the sample. After each shooting, the acquired hyperspectral image data is transferred to the computer for storage in time to avoid data loss. 三、Data preprocessing: The use of professional data processing software to pre-process the hyperspectral image data collected. First of all, the radiation correction is carried out to eliminate the radiation error caused by the performance difference of the camera itself and environmental factors, so that the spectral data between different images are comparable. Geometric correction is then performed to correct the image distortion caused by the camera Angle, sample placement, etc., to ensure that the position of each pixel in the image is accurate. The image is denoised, and the noise interference in the image is removed by filtering algorithm to improve the quality and clarity of the image, so as to extract the spectral features more accurately. 四、Spectral feature extraction: Specific algorithms and software tools are used to extract the spectral features of goose down and duck down regions respectively based on the pre-processed hyperspectral images. Through the analysis and comparison of a large number of image data, it is determined that the specific wavelength range of goose down and duck down can be distinguished significantly in the visible light to near infrared spectrum. At these key wavelengths, the reflectance values of goose down and duck down are carefully measured and recorded to form their own unique spectral feature data sets. For example, after many experimental analyses, it has been found that there are obvious differences in the reflectance curves of goose down and duck down in the wavelength range of 700nm-800nm, which can be used as an important basis for identifying the two. 五、Model establishment and verification: Based on the extracted spectral characteristic data of goose down and duck down, the spectral model for quantitative analysis of goose and duck mixed down was established by using machine learning or statistical methods. Common modeling methods include support vector machine, partial least square method and so on. In the process of modeling, a part of sample data with known mixing ratio is used as a training set to train the model, so that it can learn the internal relationship between the spectral characteristics of goose down and duck down and the mixing ratio. Another part of the sample data that did not participate in the training was used as the verification set to verify the established model. The hyperspectral image data of the validation set samples were input into the model, and the predicted mixing ratio of goose down and duck down was calculated by the model, and compared with the actual known mixing ratio. The accuracy and reliability of the model are evaluated by calculating the error between the predicted value and the true value, such as the root-mean-square error and the average absolute error. According to the verification results, the model is adjusted and optimized, such as adjusting model parameters, adding or reducing feature variables, etc., to improve the performance of the model. 6. Analysis and evaluation of results: The test results of all mixed wool samples were summarized and statistically analyzed. Statistical indexes such as mean value and standard difference of test results under different mixing ratio were calculated to evaluate the stability and repeatability of the test method. The results of hyperspectral camera detection were compared with those of traditional detection methods (such as chemical analysis) to further verify the accuracy of the hyperspectral camera detection method. Through the analysis of a large number of experimental data, the error range, detection accuracy and other key performance indexes of the hyperspectral camera in quantitative detection of goose and duck mixed velvet are obtained. The experimental results show that the method can quickly and accurately detect the precise proportion of goose down and duck down in mixed velvet in a short time, and the detection error can be effectively controlled in a very small range, which fully demonstrates its high reliability and practicability. The application of hyperspectral camera technology greatly improves the accuracy and efficiency of quantitative detection of goose and duck mixed velvet. For production enterprises, it can ensure product quality and maintain brand reputation; For the regulatory authorities, it provides strong technical support for cracking down on counterfeit and shoddy products in the market, which helps to purify the market environment and protect the legitimate rights and interests of consumers. With the continuous development and improvement of technology, it is believed that the application of hyperspectral cameras in the quantitative detection of goose and duck mixed velvet and other related fields will be more extensive and in-depth, and inject new vitality into the healthy development of the industry.
Lastest company news about Estimation of nitrogen content in walnut canopy by UAV hyperspectral camera
Estimation of nitrogen content in walnut canopy by UAV hyperspectral camera
Walnut is an important nut fruit tree and woody oil tree species in China. With its unique flavor and rich nutritional value, walnut ranks first among the four dried fruits in the world. Fruit expansion stage is the first stage of the development of walnut fruit, such as insufficient nutrition in this stage will directly affect the quality and yield of the later fruit. Therefore, the monitoring and diagnosis of nitrogen content of walnut fruit in the expanding stage is of great significance for controlling tree growth and adjusting fine management plan in time. In this study, a 400-1000nm hyperspectral camera was applied, and FS60, a product of Hangzhou Color Spectrum Technology Co., LTD., could be used for related research. The spectral range is 400-1000nm, the wavelength resolution is better than 2.5nm, and up to 1200 spectral channels can be reached. The acquisition speed can reach 128FPS in the full spectrum, and the maximum after band selection is 3300Hz (support multi-region band selection). 一、Preliminary preparation In order to estimate the nitrogen content of walnut canopy by UAV hyperspectral camera, data collection is needed first. Select the appropriate UAV platform equipped with hyperspectral camera, and carry out flight operations in accordance with the predetermined route and height over the Walnut Garden. During the flight, the hyperspectral camera imgrams the walnut canopy at a certain time interval or space interval to obtain a large amount of hyperspectral image data. At the same time, in order to ensure the accuracy and reliability of the data, it is also necessary to collect some reference data simultaneously on the ground, such as the nitrogen content of walnut leaves and canopy structure parameters determined by traditional methods. 二、Results and analysis Canopy range determination, canopy spectrum extraction and accuracy verification As shown in Figure 2, walnut, soil and shadow overlap to a certain extent in the whole band range of the 5-year old walnut forest remote sensing image. In the band range of 520~600nm, the spectral reflectance of the shadows is less than 0.10: the difference of spectral reflectance of walnut and soil is obviously not overlapping, and the spectral reflectance of both is greater than 0.10 in this range. In the range of 750-1000nm, the spectral reflectance of walnut, soil and shadow is significantly different. The spectral reflectance of walnut is greater than 0.7 in the range of 740-900nm, and the spectral reflectance of other non-target vegetation is less than 0.7. Since the spectral reflectance of walnut can be distinguished from other non-target vegetation in green light and near infrared band, but not in one or some bands, it cannot be calculated in ENVI5.3 software. Therefore, in order to facilitate the smooth extraction process of walnut canopy range, the maximum spectral reflectance of walnut canopy in green light and near infrared band is selected in this study Bw(550.7) and B(779.4) were classified and identified to determine the canopy range. Walnut tree, soil and shadow are defined in ENVI5.3 software, that is, when the spectral reflectance at B(550.7) is less than or equal to 0.10 and the spectral reflectance at B(779.4) is less than or equal to 0.20, the shadow is identified and eliminated. When the spectral reflectance at B(550.7) is greater than 0.10 and B; When the spectral reflectance at (779.4) is less than or equal to 0.70, it is identified as soil and removed; When the spectral reflectance at B(550.7) is greater than that at.0.10, the spectral reflectance at B(779.4) is greater than 0.70, walnut tree is identified as the target vegetation. In addition, a support vector machine with good generalization and classification accuracy was used to extract the canopy range, and the accuracy of the canopy range extraction based on spectral features was compared. First of all, in ENVI5.3 software, the ground objects in remote sensing images are divided into walnut tree and other two types (Figure 4), in which the red area is the walnut canopy, and the green area is the other. The separability between the two types of samples was 1.998, and then SVM classifier was selected for supervised classification to obtain the original classification results (FIG. 5a). However, there were often some small patches in the classification results, and its accuracy was difficult to achieve the purpose of final application. Therefore, the Majority small patch processing method was adopted to process the preliminary classification results, and the classification results meeting the actual requirements were obtained (Figure 5b). The accuracy of the classification results was verified, and the Kappa coefficient was 0.997, and the mapping accuracy of the target vegetation walnut was 99.65%. Finally, Matab2014b software was used to overlap the canopy range determined based on spectral features in this study with the canopy range pixels identified by support vector machine method. There were 4257 overlapping pixels in the canopy range, and the number of canopy range pixels selected based on spectral features accounted for 96.77% of the number of pixels in the support vector machine, with a mapping accuracy of 96.43 %, high precision, overlapping results are shown in Figure 6 At present, the application of UAV hyperspectral camera in estimating nitrogen content of walnut canopy is still in the stage of continuous development and improvement. In the future, with the continuous progress of technology, the performance of hyperspectral cameras will be further improved, the spectral resolution and imaging quality will be higher, and the data processing and analysis methods will be more intelligent and automated. At the same time, the development of multi-source data fusion technology, such as the combination of hyperspectral data with lidar data and thermal infrared data, will be able to obtain more comprehensive and accurate growth information of walnut trees, and further improve the accuracy and reliability of nitrogen content estimation. In addition, with the in-depth promotion of the concept of precision agriculture, UAV hyperspectral camera technology is expected to be more widely used in the field of walnut planting, providing strong technical support for the sustainable development of the walnut industry. In summary, UAV hyperspectral camera, as an advanced remote sensing monitoring technology, has a broad prospect and great potential in the application of walnut canopy nitrogen content estimation. Accurate and rapid estimation of walnut canopy nitrogen content can provide scientific basis for walnut growers to make fertilization decisions, achieve accurate fertilization, improve fertilizer utilization, reduce resource waste and environmental pollution, and promote the high-quality development of walnut industry.
Lastest company news about Rapid identification of orange peel years by hyperspectral camera
Rapid identification of orange peel years by hyperspectral camera
Orange peel has good economic value and medicinal value, but the phenomenon of fake and shoddy in the market is serious. In particular, as an important index to measure the quality of orange peel, the accuracy and efficiency of manual detection methods are low. In this paper, hyperspectral imaging technology combined with deep learning method was used to establish a fast and non-destructive identification method for the aging year of orange peel. 一、Materials and methods The purchased orange peel samples were divided into 1 year, 5 years, 10 years and 15 years according to the aging years. As shown in Figure 1, 120 orange peel samples were collected for each year, and a total of 480 orange peel samples were collected. The orange peel samples of each year were randomly divided in a ratio of 7:3, in which 84 samples entered the training set and 36 samples entered the test set. In this paper, a 900-1700nm hyperspectral camera is used, and FS-15, a product of Color Spectrum Technology (Zhejiang) Co., LTD., can be used for related research. Short-wave near-infrared hyperspectral camera, the acquisition speed of the full spectrum up to 200FPS, is widely used in the composition identification, substance identification, machine vision, agricultural product quality, screen detection and other fields. 二、Results and analysis The spectral curves of orange peel samples in different years are shown in Figure 3. The original spectral curves shown in Figure 3 can obviously find that there are absorption peaks near 1200m and 1450nm. The absorption peak at 1200nm is mainly caused by the spectral absorption of bond pairs, and the absorption peak at 1450nm is mainly caused by the spectral absorption of water. The bands of the NIR spectra of all kinds of samples overlapped closely, the overall trend was close to the same, and the absorption peak was almost in the same position, with no obvious difference. It was difficult to distinguish the four kinds of orange peel samples by naked eye. 三、Spectral pretreatment method The pretreatment of hyperspectral data of orange peel includes several steps, which are image segmentation, spectrum averaging and spectrum preprocessing. The original average spectrum of orange peel samples in different years and the average spectral curves after SG+D1 pretreatment are shown in Figure 4. It can be seen from FIG. 4(a) and FIG. 4(b) that the SG+D1 combined pretreatment method can effectively eliminate the influence of spectral baseline drift and smooth the spectral curve, thus improving the accuracy of orange peel year identification. Rapid identification of orange peel year by hyperspectral camera has broad application prospect in Chinese medicine industry. On the one hand, it can help Chinese medicine manufacturers and dealers accurately control the quality and year of orange peel, and avoid economic losses and reputation risks caused by misjudgment of year. On the other hand, in terms of market supervision, relevant departments can use the technology to carry out rapid sampling of orange peel products on the market, crack down on shoddy and other behaviors, and maintain the normal order of the market. In addition, with the continuous improvement and popularization of the technology, it will also provide strong support for the scientific research and quality evaluation of orange peel, and promote the development of orange peel industry in a more standardized, standardized and scientific direction.
Lastest company news about Application of hyperspectral imaging technology to the detection of protein content in milk
Application of hyperspectral imaging technology to the detection of protein content in milk
In the evaluation of dairy nutrition, the protein content is the most important indicator that milk is an essential source of protein absorption in People's Daily life. In recent years, the health of consumers and the development of the dairy industry are closely related to the quality of milk. Therefore, the detection of milk protein content is a very important link. Traditional detection methods consume a long time, waste a lot of human resources, and lead to environmental deterioration. Therefore, it is of great significance to find a faster and more accurate method for detecting milk protein content. Therefore, this paper uses machine learning combined with hyperspectral imaging technology to quantitatively evaluate milk protein content, providing a feasible scheme for milk protein content detection on the market. Specific research work and conclusions are as follows:   一、Experimental materials We bought seven different brands of pure milk, including Mengniu, New Hope, Yili and Guangming, and stored them in the refrigerator. Protein content is shown in Table 1. 二、Experimental equipment In this paper, a 400-1000nm hyperspectral camera is used. FS13, a product of Hangzhou Color Spectrum Technology Co., LTD., can be used for related research. The spectral range is 400-1000nm, the wavelength resolution is better than 2.5nm, and up to 1200 spectral channels can be reached. The acquisition speed can reach 128FPS in the full spectrum, and the maximum after band selection is 3300Hz (support multi-region band selection). 三、Experimental setting method The hyperspectral images of milk samples were collected by using the hyperspectral spectrometer. Samples were collected three times for each type of milk, and then a clear image was selected from ENVI5.3. The collected spectral image had a resolution of 777x1004 pixels. The exposure time of the hyperspectral imager was 10ms, the pixel mixing times were 6, the resolution was 4.8nm, the average interval was 0.8nm, the vertical distance was 30cm, and the acquisition condition was room temperature (23~25°C). The imaging spectrometer and scanning head are installed together during the shooting, and the average spectral data of the milk is derived from the hyperspectral image using the ENVI software." 四、Extraction and preprocessing of hyperspectral data Extracting hyperspectral reflectance data from hyperspectral images is the basis of traditional machine learning modeling. Generally, the spectral reflectance data of samples is obtained by extracting the average spectral reflectance of all pixels in the region of interest (ROD). In this paper, ENVI software was used to open the corrected hyperspectral image of milk sample, and the pixel near the center of each hyperspectral image was selected as the ROI with the rectangle tool. A total of 30 ROI and 7 hyperspectral images were selected, and 210 ROI were selected. The average spectral reflectance of all pixels in ROI was calculated as the spectral data of the sample, a total of 210 spectral data. The spectral data is saved in ASCI format. The following figure shows the process of extracting ROI. In this paper, hyperspectral imaging technology combined with machine learning was used to predict milk protein content in order to improve the accuracy of milk protein content prediction. Hyperspectral imaging system was built, hyperspectral images of 7 kinds of milk brands on the market were collected, spectral data were extracted by ENVI software, milk hyperspectral data set was established, and 210 hyperspectral data were extracted finally. Hyperspectral imaging technology has shown great potential in the field of milk protein content detection, although there are some challenges at this stage, but with the integration of interdisciplinary technology innovation, it will gradually revolutionize the traditional milk detection mode. Through continuous optimization of the technical system and solving practical application problems, hyperspectral imaging will become an indispensable and powerful tool for dairy quality control, help improve the economic and social benefits of the milk industry, and meet the growing demand of consumers for high-quality dairy products.
Lastest company news about Determination of amylose content in fresh lotus by hyperspectral imaging
Determination of amylose content in fresh lotus by hyperspectral imaging
With the improvement of living standards, people have higher and higher requirements for the taste and nutrition of lotus seeds. Lotus seed as a medicine is also a kind of tonic, its amylose content directly affects the quality and taste of lotus seed. The amylose content of lotus seeds varies greatly among different varieties, so the determination of amylose content of lotus seeds is of great significance for subsequent processing. The traditional amylose detection is generally using iodine colorimetry, iodine affinity titration and cross-cutting infection method, these methods are time-consuming and laborious, and easy to be affected by experimental conditions! Hyperspectral imaging technology is a non-destructive testing technology that can obtain rich spectrum and image information. Compared with chemical detection methods, it has the advantages of saving time, labor and environmental protection. In this paper, hyperspectral imaging technology was used to detect amylose of fresh lotus. 一、Materials and methods   1.1 Test materials The samples were from Fujian province, and the varieties of Xuanlian, Guangchanglian, Jianxuan 36, Mantianxing, Space lotus and Xianglian were selected. After picking at maturity, the fresh lotus seed was stored in liquid nitrogen and transported to the laboratory, where it was refrigerated at 4 ° C for 12 hours. 1.2 Hyperspectral image acquisition and correction The main components of hyperspectral imaging system include hyperspectral imager, light source, stage, black box and hyperspectral data acquisition software. The whole system can use the color spectrum hyperspectral camera fs-13, which can collect the spectral range of 400nm~1000nm, and the spectral resolution is 2.5nm. The hyperspectral imaging system is shown in Figure 1. The moving speed of the payload platform is set to 3.5mm/s and the exposure time is 30ms. The lens is 40cm away from the moving platform and straight down. Adjust the focal length of the camera of the spectrometer for black and white correction of the system. 1.3 Data Processing Analysis software was used to extract the average spectrum of the region of interest (ROI) from the spectral image of lotus seeds. In order to eliminate the influence of noise and external stray light, the modeling effect of pre-processing methods such as first derivative, second derivative, SG smoothing, multiple scattering correction (MSC) standard normal variable conversion was compared, and the best pretreatment method was selected. 二、Results and analysis   2.1 Average spectrum of the region of interest In this paper, the spectral curve of each pixel in the region of interest of a single sample is used for subsequent processing. The average spectral diagram after removing the head and tail noise (400nm~971nm) is shown in Figure 2. It can be seen from the figure that the variation trend of spectral values of different samples is consistent. The band has an obvious upward shift between 460nm and 570nm, which may be caused by the shift in the water band. The band has a relatively obvious absorption between 500nm and 920nm. It may be related to quaternary frequency doubling, O-H secondary frequency doubling and O-H primary frequency doubling of C-H group in amylose molecule. 2.2 Amylose content of lotus seeds The results of correction set and prediction set of amylose content divided by SPXY method are shown in Table 1. It can be seen from the table that the amylose content of fresh lotus seeds varies greatly. The maximum value of amylose content of corrected lotus seeds is 227.90 mg/g, the minimum value is 100.82 mg/g, and the standard deviation is 44.73mg/g. The amylose content of the predicted sample is within the range of the correction set sample, so the sample division is reasonable. 三、Conclusion In this paper, hyperspectral imaging technology was used to rapidly detect amylose content. The results show that the modeling effect is the best after using first derivative and multiple scattering correction MSC). Then SPA was used to extract 9 feature bands. The corrected set correlation coefficient (R) of the PLSR prediction model was 0.835, the corrected set root mean square error (RMSEC) was 1.802, the predicted set correlation coefficient (R) was 0.856, and the predicted set root mean square error (RMSEP) was 1.752. The relative analysis error (RPD) was 1.944. The correlation coefficient of prediction set of PLSR prediction model established by RC method (R. The prediction set root mean square error (RMSEP) was 1.897. The relative analysis error (RPD) was 1.761. This study provided a thought for further developing an on-line detection instrument for amylose content, and laid a good foundation.
Lastest company news about Application of hyperspectral camera to detect pumpkin seed vitality
Application of hyperspectral camera to detect pumpkin seed vitality
As an important cash crop, pumpkin seed vitality is directly related to the emergence rate, seedling growth potential and final yield after sowing. The traditional methods of seed vitality detection, such as germination test, are time-consuming and laborious, and can not meet the needs of rapid and large-scale seed quality detection in modern agriculture. Hyperspectral imaging technology combines the advantages of spectroscopy and imaging, and can obtain the spectral information and spatial information of samples at the same time, which shows great potential in the field of seed viability nondestructive testing. 一、Preparation of experimental materials Divide the pumpkin seeds into 4 groups of 100 seeds and place them in a nylon mesh bag, as shown in Figure 3-2. Put a group of pumpkin seeds in the dryer every other day. The specific procedure is as follows: take out 3 groups of samples, put the first group of samples in the dryer, put the second group of samples in the dryer 24 hours later, put the third group of samples in the dryer 24 hours later, and take out all the samples with aging time of 1 to 3 days respectively after 3 days (the first group is the samples with aging time of 3 days). Group 2 is for samples aged for 2 days, and Group 3 is for samples aged for 1 day). The remaining 1 of the 4 groups was not subjected to aging treatment and was placed at room temperature for 3 days during the aging group experiment. 二、Hyperspectral data acquisition Seeds with different aging days were collected by a color spectrum hyperspectral camera, and hyperspectral images of 400-1000nm were taken for all samples. After the spectral data were extracted, a total of 400 spectral curves were obtained, as shown in the figure. Observe the growth every day, and pour the right amount of water to ensure the water needed for germination. The germination was recorded once on the third and fifth days respectively. The following is the pre-germination test diagram of pumpkin seeds. According to the vitality level of each seed, the average spectral data of each seed was classified, and the overall spectral curve of each grade was shown in the figure below. 三、Spectral data processing The original hyperspectral image is susceptible to noise and uneven illumination. Median filter is adopted to remove salt and pepper noise, and the illumination difference is eliminated based on the reflectivity correction of the standard whiteboard. The region of interest (ROI) is extracted from the corrected image, focusing on the seed embryo and endosperm to ensure the accuracy of subsequent feature extraction. Dimensionality reduction methods such as principal component analysis (PCA) are used to compress data initially, retain key information and reduce computation. 四、Conclusion and Prospect In this study, a pumpkin seed vitality detection model based on hyperspectral imaging technology was successfully constructed to realize rapid, non-destructive and high-precision vitality identification, and provide an efficient technical solution for the quality control of pumpkin seed industry. Follow-up research can be extended to more crop seeds, and multi-modal data (such as fluorescence spectrum, thermal imaging, etc.) can be integrated to further improve the detection accuracy in complex environments. Combined with Internet of Things technology, an online monitoring system for seed vitality can be built to help real-time control and accurate screening of seed quality in smart agriculture.
Lastest company news about Application of hyperspectral camera to tea pests and diseases
Application of hyperspectral camera to tea pests and diseases
Tea inchworm is one of the common pests in tea gardens, which seriously affects the yield and quality of tea. The traditional method of monitoring the damage degree of tea inchworm mainly relies on manual investigation, which has some problems such as low efficiency, strong subjectivity and difficult to realize real-time monitoring in large area. Hyperspectral remote sensing technology has the characteristics of high spectral resolution and rich spectral information, which provides a new way for rapid and accurate monitoring of the harm degree of tea inchworm. 一、Environmental conditions The spectral reflectance of tea canopy was measured from 10:00 to 14:00 on a sunny day with no wind, no cloud and good solar visibility. In this study, a 400-1000nm hyperspectral camera was applied, and FS13, a product of Hangzhou Color Spectrum Technology Co., LTD., could be used for related research. During the observation, the field of view Angle was 25, and the height between the detection head of the hyperspectral camera and the top of the tea canopy was about 0.5m. The diameter of the observation range was about 0.22m. In order to reduce the experimental error, the measurements were repeated three times in each sample area, and the average value was taken as the spectral reflection value.   二、 Data processing and analysis 1. Comparison of leaf surface appearance between normal tea and tea inchworms. In this experiment, a series of tea leaves harmed by tea inchworms to different degrees were collected as research subjects, and their spectral data, leaf area index and the number of tea inchworms per mu of tea ruler were respectively collected. The comparison between tea leaves without insect pests and those harmed by tea inchworms was shown in Figure 1: The leaves were intact, the leaves were crowded together, and the leaves of the insect-damaged tea were bitten into irregular shapes, their external color became dark yellow, and the structure of the leaves also changed accordingly. 2. Comparison of leaf area index between normal tea and tea inchworm. As can be seen from FIG. 2, the leaf area index was greatly affected by the degree of harm caused by tea geometrid. The more tea ruler there were, the more tea leaves were eaten, and the smaller the leaf area index would be. 3. The influence of tea inchworms on the reflectance spectral characteristics of tea canopy. The influence of insect infestation on tea leaves will lead to some changes in the physical and chemical properties of tea leaves, including the color, structure, water content, chlorophyll content and nutritional status of the leaves. The change of these physical and chemical properties will cause some changes in the value of its spectral characteristic parameters, such as spectral reflectivity, transmittance, absorptivity, red peak and its wavelength position and blue peak and its wavelength position. Therefore, to grasp the normal tea spectral characteristics and related information is the premise and basis of studying the damage of tea by other diseases and pests. 三、Research significance and prospect Research significance: This study provides a new technical means for the rapid and accurate monitoring of the harm degree of tea inchworms, helps to timely grasp the occurrence of tea inchworms in tea gardens, provides scientific basis for the accurate prevention and control of diseases and pests in tea gardens, reduces the use of pesticides, and improves the yield and quality of tea. Research prospects: Future studies can further optimize hyperspectral remote sensing models and improve the accuracy and stability of the models. At the same time, it can be combined with UAV remote sensing, satellite remote sensing and other technologies to achieve a larger range of tea inchworm harm degree monitoring. In addition, the relationship between the harm of tea inchworms and the physiological and ecological changes of tea trees can be deeply studied, and the mechanism of hyperspectral remote sensing monitoring can be revealed from a deeper level.