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Baoquan Y.,Key Laboratory of Agricultural Information Acquisition Technology | Baoquan Y.,China Agricultural University | Ruizhi S.,Key Laboratory of Agricultural Information Acquisition Technology | Hongjun Y.,Key Laboratory of Agricultural Information Acquisition Technology
International Journal of Smart Home

With the development of technology and communication network, smartphones are increasingly used in emergency platforms. Under disaster environment, however, the information reporting system may confronted with poor communication conditions or low phone battery. In this study we first defined two parameters: the communication signal and the battery status. Based on the definitions, we proposed an algorithm for smartphone based reporting system under disaster environment. With the guarantee that disasters can be reported promptly and accurately and based on the analysis of smartphone power consumption, the algorithm provides power saving mechanisms to obviously increase the system working time. At last, a smartphone based disaster information reporting system (MDIRS) is designed and applied in disaster environment. © 2015 SERSC. Source

Xiang M.,China Agricultural University | Wei S.,Key Laboratory of Agricultural Information Acquisition Technology | He J.,China Agricultural University | Qiu R.,China Agricultural University | Zhang M.,China Agricultural University
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Agricultural implement automatic navigation is a major trend of modern agriculture. Agricultural implement navigation can avoid the errors during implement shaking in field and improve the precision of navigation and flexibility of operation. At present, most of the navigation terminals were developed based on the industrial computer which is expensive and difficult to be spread. In this paper, an agricultural implement visual navigation terminal based on DSP and MCU for automatic weeding was designed. As the core processor of the system, DSP was responsible for the image acquisition, crop rows detection and offset calculation of navigation line. MCU is the main control unit of the system, so it was used to manage the working process, receive, store and forward GNSS data, and also control the implement. The corresponding protocol of serial communication, network communication and CAN bus communication in the system was normalized to make sure the stability of the communication. In the image processing, the OTSU method and the crop row detection algorithm based on boundary detection and scan-filter (BDSF) were adopted, which could improve the accuracy and efficiency of navigation line detection. In order to verify the validity and stability of the system, the algorithm adaptive test, offset accuracy test and different system comparison tests were carried out. The experiment results showed that the crop line detection algorithm could work adaptively in the weed and crop thinning conditions. The average error of offset detection is 1.29 cm and the maximum error is 4.1 cm. The systems comparison test verified the economic feasibility of the proposed system by compared with the PC and ARM, which can satisfy the requirements of filed operation. © 2015, Chinese Society for Agricultural Machinery. All right reserved. Source

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

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. Source

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

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. Source

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

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. Source

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