Key Laboratory of Agricultural Information Acquisition Technology

Beijing, China

Key Laboratory of Agricultural Information Acquisition Technology

Beijing, China
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Deng C.,China Agricultural University | Deng C.,China Tobacco Guangxi Industrial Co. | Song J.,Chinese Academy of Sciences | Sun R.,China Agricultural University | And 7 more authors.
Tobacco Science and Technology | Year: 2016

As a solution to the massive marketing data and unsatisfactory analysis efficiency, a visual analysis system of marketing data (VASMD) for cigarette enterprises was developed based on thermodynamic diagram. VASMD adopted a triple-layer distributed data processing architecture, including data collecting layer, data preprocessing layer, and visual analysis layer. Data analysis models were built by using k-means and DBSCAN data mining algorithms, an interactive visual analysis diagram and several auxiliary analysis tools were developed based on Baidu map. In the case studies, two marketing data sets of cigarette brand"Zhenlong" produced by China Tobacco Guangxi Industrial Limited Corporation were used for screening, preprocessing and visual analysis. The results showed that: 1) The development of sale hot spots in main cities had strong guiding effects on the hot spot formation of their neighboring counties, towns and villages. 2) The hot spots distributed and spread mostly along the major and trunk traffic networks. 3) There existed mutual traction effects between those regions with frequent economic contacts and similar language and culture in the formation and spreading of hot spots. In conclusion, a VASMD in the form of thermodynamic diagram can figuratively demonstrate the geographic distribution and structure of cigarette consumer groups, reveal the market development patterns and rules implied by the data, and promote the level of precise marketing of enterprises effectively. © 2016, Editorial Office of Tobacco Science and Technology. All right reserved.


Wei S.,China Agricultural University | Li S.,Key Laboratory of Agricultural Information Acquisition Technology | Zhang M.,China Agricultural University | Ji Y.,China Agricultural University | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2017

In order to improve the performance of the agricultural machinery automatic navigation system, an automatic navigation path searching method of agricultural machinery based on GNSS (global navigation satellite system) was proposed. According to different demands of farm working, the system could generate the straight line or curve path for agricultural machinery automatic navigation according to users setting. In order to get the straight navigation path, the user should drive the tractor and record the current position marked as point A, and then choose the position marked as Point B at least 10 m far away from Point A. The straight line presupposed navigation path could be obtained by connecting Point A and B and extending the segment AB. The way of obtaining the curve presupposed navigation path is similar to the straight path searching method; the curve fits with several segments, and every segment is analyzed with the straight path searching method. When the navigation task began, the system would compare the current position and heading information of the tractor with the presupposed path to get the lateral deviation and heading deviation. In addition, a pure pursuit mode based on preview points research was proposed for steering control. The method didn't involve the complicated control theory, so that it could adapt the navigation system better. In the aspect of turning control, arcuate turning and pyriform turning patterns were selected as the major research objects. The turning path could be generated by the navigation system according to the tractor working width and the minimum turning radius after the users chose the kind of turning pattern, and a series of points could be chosen according to the tractor speed and each point was evenly spaced. When the navigation task began, the searching radius and preview point should be set according to the speed of the tractor. There were several points on the default navigation path falling in the searching circle; the point with the largest ID (identification) number would be selected as the preview point, and then the path of the tractor arriving to the preview point and the control turning angle would be obtained. To verify the path search method and the model of pure tracking performance, a tractor automatic navigation software was designed and implemented. The industrial computer as the carrier of navigation software, processed the GNSS data, IMU (inertial measurement unit) data and PLC (programmable logic controller) data, and then generated the corresponding decisions. A John Deere tractor was used as the platform for experiments, and the straight line / curve navigation experiments based on GNSS positioning technology were designed. The results of experiments were as follows: In the straight line navigation experiments, when the speed of the tractor was 0.8, 1.0 and 1.2 m/s, the root-mean-square error was 3.79, 4.28 and 5.39 cm respectively; in the turning navigation experiments, when the speed of the tractor was 0.6 m/s, the root-mean-square error of the arcuate turning navigation was 25.23 cm and the root-mean-square error of the pyriform turning navigation was 14.42 cm; for the comparison experiment, the root-mean-square error using the proposed method and fuzzy control method was 4.30 and 5.95 cm respectively in straight line navigation module, and 13.73 and 21.40 cm respectively in curve navigation module. The path searching method and the pure pursuit mode based on the researching of preview points can satisfy the requirement of the farmland works effectively. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.


Cai Y.,China Agricultural University | Cai Y.,Key Laboratory of Agricultural Information Acquisition Technology | Ma L.,China Agricultural University | Ma L.,Agricultural University of Hebei | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2017

With the enhancement of living conditions, the demand for milk is increasing rapidly, the quality of milk is paid more and more attention, and the improvement of the quality of milk has already become an important issue. However, subclinical mastitis in dairy cows is the most dangerous and costly disease which is difficult to control in dairy farm. In recent years, about 1/3 cows of the world are suffering from mastitis, especially subclinical mastitis in dairy cattle. Among them, the incidence of subclinical mastitis is 40%-80% in China, which is seriously harmful to the healthy development of dairy industry. In order to solve the problem of rapid detection of subclinical mastitis in dairy cows, a fast test system based on the computer vision technology of subclinical mastitis was proposed in this paper. Firstly, 25 dairy cows were selected randomly in the experiment, including 5 dairy cows with recessive mastitis, 5 dairy cows with severe mastitis and other 15 healthy dairy cows. Each cow has 4 breasts, so there were 100 sets of data in total. The Foss 5 000 milk somatic cell counts detector was used to obtain the number of somatic cells per sample. At the same time, the samples were dropped on the pH test paper, whose images were collected by USB (Universal Serial Bus) camera connected with the computer. The collected milk pH test paper images were changed into 500 × 500 pixels, and transformed from RGB (red, green, blue) color space to HSV (hue, saturation, value) color space. According to the color characteristics of the pH test paper, the threshold value was selected and the collected images were binarized. On the other hand, the segmented image was processed by morphological processing to remove the segmentation error and edge burr. Finally, the segmentation results were achieved by fusing the 2 results. Linear regression, power regression, quadratic regression, and principal component regression were used to establish estimation models using 75 sets of data. Those models were compared using the remaining 25 sets of data. The power regression of the principal component had a higher correlation coefficient, a lower standard error, and the highest determination coefficient (R2) of 0.970. System function and user interface were designed based on Android programming technology. The second experiment was carried out in the cattle farm to validate the favorable model by using the designed mobile terminal equipment which was connected with the USB camera. Using the 20 sets of data to validate the model, the correlation coefficient of the estimated milk somatic cell counts and the measurement value was 0.970, the estimated average relative error was 3.67%, and the standard deviation was 1.88%. The established estimation model of milk somatic cell counts using R and G indices estimated the milk somatic cell counts better than the model using only one index and the model combining 3 indices. Through the model comparison using the 100 sets of data and the validation in the real farm, the detection system of milk somatic cell count is more accurate, and can be used for the rapid detection of subclinical mastitis in dairy cows. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.


Qiu R.,China Agricultural University | Zhang M.,China Agricultural University | Wei S.,Key Laboratory of Agricultural Information Acquisition Technology | Li S.,Key Laboratory of Agricultural Information Acquisition Technology | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2017

Stem diameters of maize are important phenotype parameters and can characterize the crop growth and lodging resistance, drawing more attentions from breeders. Traditional measurement about stem diameters is usually manual measurement, which is timeconsuming, laborious, and subject to human error. In order to rapidly measure stem diameters of maize in field, a method based on RGB-D (red, green, blue - depth) camera was proposed in this paper to extract stem diameters of maize. The color images and depth images of the maize plants at the small bell stage were captured by a RGB-D camera in field. First, maize stem was extracted by processing the color image. It was hard to recognize maize just according to the color differences in red, green and blue component between maize and background due to the illumination variations. To solve the problem, the component that represented the difference between green signals and illumination brightness was calculated and applied to segment maize with Otsu algorithm, and the binary image of maize was generated. And then erosion operation was conducted within region of interest to cut off the connection between little leaves and maize stem, and small regions were eliminated to remove weed and little leaves. The largest region of maize was saved after dilation operation. After that, skeletonization was conducted for main stem. There were crossing points at the points of contact between leaves and stem, and ending points at the points of contact between ground and stem, and the potential measurement region of stem could be identified by searching crossing points and ending points. The color coordinates of the potential measurement region were saved and corresponding point cloud data were generated based on the mapping relationship between color coordinate, depth coordinate and camera coordinate. Second, stem diameters were calculated by processing point cloud data. Noise points affected measurement accuracy of stem diameters, and K-nearest method was applied to remove scattered points from point cloud data. Then the filtered point cloud data of potential measurement region were clustered. There were some point cloud data on the edge of stem due to the measurement of time of flight (ToF), which were background noises. K-means method was used to divide the filtered point cloud data into 2 groups, and only the group whose central point was nearer to the camera was saved to represent maize stem. The saved point cloud data were one side of stem, and ellipse fitting based on least square method was carried out for the point cloud data. Long axis parameter and short axis parameter of ellipse were calculated respectively to indicate the stem diameters of maize. 20 samples were tested to verify aforementioned method, and the experimental results showed that the method proposed in this paper had a good performance in segmenting and identifying maize stem, though ellipse fitting method needed to be improved. The mean errors, standard deviation and mean relative errors of measuring stem diameters were 3.31 mm, 3.01 mm, 10.27% for long axis and 3.33 mm, 2.39 mm, 12.71% for short axis, respectively, indicating that the proposed method could be applicable for plant phenotyping. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.


Wu Z.,China Agricultural University | Wu Z.,Key Laboratory of Agricultural Information Acquisition Technology | Sun M.,China Agricultural University | Sun M.,Key Laboratory of Agricultural Information Acquisition Technology | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2017

With the growing deterioration of water environment, heavy metal pollution has become increasingly prominent, and causes a matter of concern. Heavy metals have a strong interaction with a variety of enzymes and proteins in the human body, where the protein and enzymes lose activity. Heavy metals maybe enrich in certain organs of the body, and if its content exceeds the limits of the content that human body can tolerate, it will cause human acute poisoning, subacute poisoning, and chronic poisoning. Development of portable rapid detection equipment of heavy metal becomes necessary. In this study, heavy metal lead ions (Pb2+) are the research object, and the research is based on colorimetry theory, Lambert-Beer's law and spectrometry to develop a portable detector for Pb2+. According to the analysis of the physical and chemical properties of Pb2+ and the research of colorimetric reaction between Pb2+ and dithizone, existing colorimetric detection method of Pb2+ is improved, and complex and cumbersome pre-process of detector is simplified. Under suitable conditions of room temperature, pH value of 9.0 and certain volume, Pb2+ reacts with dithizone solution whose color is blue-green, which can generate orange complex, so that the measured value of Pb2+ can be converted to easily measured mathematic data to build detection model for the determinator, and other metal ions do not interfere in the determination by adding the masking agents. The determinator designed includes the optical circuit part and electric circuit part. The optical circuit part consists of light source, optical fiber, and silicon detector, which is used to collect the optical signal. Optical module uses the 510 nm wavelength LED (light- emitting diode) with narrow band filter as the active light source, and optical fiber is the transmission channel to make sure the monochromatic light source gives the parallel and vertical light striking the detector. Additionally, a silicon photodetector is the optical detector. The optical part is aimed to detect the band transmittance. Electric circuit is designed to convert the light signal to electrical digital signal, amplify the signal, decrease the noises, data process and store, and real-time display and communication. The electric circuit includes microcontroller, LED drive circuit, detection circuit, communication circuit, keyboard circuit, and liquid crystal display circuit, and lithium-ion battery is used as power supply. PS0308 type photodiode with spectral response range 300-1 100 nm converts the optical signal to electric signal and also effectively guarantees the linearity of the instrument. The system software is used to detect electric quantity, measure and manage data, and so on. All the connection parts are fixed together through the metal pieces to prevent deviation by the movement of light path. When the device has been installed, system performance is analyzed to assure the accuracy. Power consumption and anti-interference of the software and hardware are tested, also repetitive testing is done to verify the accuracy of the measurement. Experimental results show that decision coefficient of the predicted and the real concentration values in training set is 0.934, and the value is 0.822 2 in prediction set, and the detection range of determinator is 0.01-0.2 mg/L, indicating it can detect the Pb2+ with lower concentration. Relative standard deviation of Pb2+ concentration is less than 1.0%. The test results indicate that this determinator is simple to operate with satisfactory precision, accuracy, and repeatability, realizing the miniaturization of instrument and on-site rapid detection. At the same time, the determinator has simple and stable structure with low consumption. Based on the instrument, in the future work, the portable rapid detection of Pb2+ can be adapted to a variety of heavy metals in aqueous media with a low cost, low detection limits, and simple pre-treatment. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.


Huang J.,China Agricultural University | Huang J.,Key Laboratory of Agricultural Information Acquisition Technology | Sedano F.,University of Maryland University College | Huang Y.,U.S. Department of Agriculture | And 8 more authors.
Agricultural and Forest Meteorology | Year: 2016

The scale mismatch between remote sensing observations and state variables simulated by crop growth models decreases the reliability of crop yield estimates. To overcome this problem, we implemented a two-step data-assimilation approach: first, we generated a time series of 30-m-resolution leaf area index (LAI) by combining Moderate Resolution Imaging Spectroradiometer (MODIS) data and three Landsat TM images with a Kalman filter algorithm (the synthetic KF LAI series); second, the time series were assimilated into the WOFOST crop growth model to generate an ensemble Kalman filter LAI time series (the EnKF-assimilated LAI series). The synthetic EnKF LAI series then drove the WOFOST model to simulate winter wheat yields at 1-km resolution for pixels with wheat fractions of at least 50%. The county-level aggregated yield estimates were compared with official statistical yields. The synthetic KF LAI time series produced a more realistic characterization of LAI phenological dynamics. Assimilation of the synthetic KF LAI series produced more accurate estimates of regional winter wheat yield (R2=0.43; root-mean-square error (RMSE)=439kgha-1) than three other approaches: WOFOST without assimilation (determination coefficient R2=0.14; RMSE=647kgha-1), assimilation of Landsat TM LAI (R2=0.37; RMSE=472kgha-1), and assimilation of S-G filtered MODIS LAI (R2=0.49; RMSE=1355kgha-1). Thus, assimilating the synthetic KF LAI series into the WOFOST model with the EnKF strategy provides a reliable and promising method for improving regional estimates of winter wheat yield. © 2015 Elsevier B.V.


Zhang H.,China Agricultural University | Zhang H.,Tianjin Agricultural University | Zhang H.,Applied Technology Internet | Zhang H.,Key Laboratory of Agricultural Information Acquisition Technology | And 5 more authors.
Computers and Electronics in Agriculture | Year: 2014

Cotton is an important crop throughout the world, and its quality plays a significant role in its profitability and marketability. Foreign matter in cotton can cause damage to spinning, weaving, and dyeing and thus seriously affects the quality of cotton products. Conventional methods including inspection by human workers and instrument based approaches such as photoelectric detection and ultrasonic detection are time-consuming, labor-intensive, and sometimes inaccurate. As a non-destructive, cost-effective, rapid, and objective inspection tool, computer vision has been widely used in cotton foreign matter inspection. In this review, the basic concepts, components, and image acquisition modes of computer vision techniques are presented. The improvements in image processing and analysis of foreign matter in cotton are introduced, and several different computer vision systems that have been created to detect foreign matter are reviewed to highlight the potential for the inspection of foreign matter. Considering the progress made to solve this type of problem, we also suggest some directions for future research. © 2014 Elsevier B.V.


Yilong Z.,Jiangsu University | Yilong Z.,Key Laboratory of Agricultural Information Acquisition Technology | Yilong Z.,Applied Technology Internet | Dean Z.,Jiangsu University | And 2 more authors.
International Journal of Electrochemical Science | Year: 2015

Nitrite has been widely used in industrial and agricultural production and is ubiquitous in food, water, biology and the environment. However, nitrite is also a toxic inorganic contaminant that is hazardous to the health of humans and other organisms. A variety of strategies have been proposed for detecting and monitoring nitrite in recent years. This article was compiled as a general review of the strategies proposed for nitrite detection, and relevant detection parameters (such as materials, detection limit, detection range, working pH and stability) were tabulated. This article is organized by the type of signal obtained from strategies, including electric and optical signals. Electrochemical methods receive an electric signal from dissolved nitrite, with voltammetric, potentiometric and impedimetric methods included. Methods that receive an optical signal include fluorescence, absorption and Raman spectrometry. Biosensors are proposed as a new detection method. The advantages/disadvantages and limitations of the techniques are discussed. Finally, methods employed to perform nitrite detection are summarized, and their future development is discussed. © 2015 The Authors.


Zeng L.,Key Laboratory of Agricultural Information Acquisition Technology | Zeng L.,Agricultural University of Hebei | Li D.,Key Laboratory of Agricultural Information Acquisition Technology | Li D.,Applied Technology Internet
Journal of Sensors | Year: 2015

Chlorophyll fuorescence measurement is a sensitive and effective method to quantify and analyze freshwater and sea water phytoplankton in situ. Major improvements in optical design, electronic technology, and calibration protocol have increased the accuracy and reliability of the fuorometer. This review briefy describes the improvement of probe design, excitation light sources, detectors, and calibrations of in situ fuorometers. Firstly, various optical designs for increasing the efficiency of fuorescence measurement are discussed. Next, the development of electronic technology to meet and improve in situ measurement, including various light sources, detectors, and corresponding measurement protocols, is described. In addition, various calibration materials, procedures, and methods are recommended for different kinds of water. The conclusion discusses key trends and future perspectives for in situ fuorescence sensors. Copyright © 2015 L. Zeng and D. Li.


Meng Q.,China Agricultural University | Qiu R.,China Agricultural University | Zhang M.,China Agricultural University | Liu G.,China Agricultural University | And 2 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2015

Taking agricultural vehicle with machine vision navigation as study object, a self-adaptive fuzzy control method with improved particle swarm optimization algorithm was designed. Firstly, by establishing 2-DOF steering model and visual preview model, lateral control equations of vehicle were described. Secondly, in order to improve the convergent speed of particle swarm optimization (PSO) algorithm, an improved PSO algorithm was designed. Finally, agricultural vehicle guidance system was a complex system with high nonlinearity, time-varying and large delay; therefore, an adaptive fuzzy controller was used for path tracking control. Correction factors were introduced into the fuzzy controller and particle swarm algorithm was used to optimize the correction factors. Taking the integral time absolute error (ITAE) sum of lateral offset and heading offset as the objective function, optimal correction factors were calculated by using PSO algorithm. Simulation and experimental results showed that the designed control algorithm could eliminate the lateral offset rapidly with less overshoot and rapid response. It retained the advantages of fuzzy control method and improved the control quality of guidance system. Compared with standard fuzzy control method, the improved fuzzy control method has a significant improvement on navigation accuracy under the same parameters condition. When the velocity of vehicle was 0.8 m/s, the maximum lateral offset of straight path and curve path were less than 4.2 cm and 5.9 cm respectively, which could meet the requirement of agricultural vehicle navigation. ©, 2015, Chinese Society of Agricultural Machinery. All right reserved.

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