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Gu X.,Beijing Research Center for Information Technology in Agriculture | Wang Y.,Beijing Research Center for Information Technology in Agriculture | Song X.,Beijing Research Center for Information Technology in Agriculture | Xu X.,Beijing Research Center for Information Technology in Agriculture
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

Monitoring dry biomass of crop timely and accurately by remote sensing is crucial to assess crop growth, manage field water-fertilizer and predict yield. The Huaihe River Basin in China was chose as study area to map the spatial distribution of paddy biomass. The study derived 12 vegetation indexes from HJ-CCD image, which were closely related to crop growth. After screening sensitive vegetation index with in-situ samples by correlation analysis, the study developed the inversion model by single variable and multiple variables. The determination coefficient (R2) and root mean square error (RMSE) was used to evaluate the accuracy of models. Results showed that the accuracies of multivariable models were better than these of single-variable models, of which the average R2reached 0.647 and the average RMSE was 0.059. It indicated that the multi-variable models were input in more information than those of single-variable models, which improved the accuracies of estimating paddy biomass in to a certain degree. The average overall accuracies of multi-variable models were 92.7%, while that of singe-variable models were 87.8%. The model with multiple linear regressions could be used to map the paddy biomass in the study area by using HJ-CCD image. © 2016 SPIE.


Gu X.,Beijing Research Center for Information Technology in Agriculture | Wang L.,Beijing Research Center for Information Technology in Agriculture | Song X.,Beijing Research Center for Information Technology in Agriculture | Xu X.,Beijing Research Center for Information Technology in Agriculture
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

Leaf nitrogen accumulation (LNA) has important influence on the formation of crop yield and grain protein. Monitoring leaf nitrogen accumulation of crop canopy quantitively and real-Timely is helpful for mastering crop nutrition status, diagnosing group growth and managing fertilization precisely. The study aimed to develop a universal method to monitor LNA of maize by hyperspectrum data, which could provide mechanism support for mapping LNA of maize at county scale. The correlations between LNA and hyperspectrum reflectivity and its mathematical transformations were analyzed. Then the feature bands and its transformations were screened to develop the optimal model of estimating LNA based on multiple linear regression method. The in-situ samples were used to evaluate the accuracy of the estimating model. Results showed that the estimating model with one differential logarithmic transformation (lgP') of reflectivity could reach highest correlation coefficient (0.889) with lowest RMSE (0.646 g·m-2), which was considered as the optimal model for estimating LNA in maize. The determination coefficient (R2) of testing samples was 0.831, while the RMSE was 1.901 g·m-2. It indicated that the one differential logarithmic transformation of hyperspectrum had good response with LNA of maize. Based on this transformation, the optimal estimating model of LNA could reach good accuracy with high stability. © 2016 SPIE.


Yang G.,Beijing Research Center for Information Technology in Agriculture | Yang G.,Beijing Normal University | Pu R.,University of South Florida | Zhao C.,Beijing Research Center for Information Technology in Agriculture | And 2 more authors.
Remote Sensing of Environment | Year: 2011

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990m and 90m resolutions, respectively. Secondly, the relationship between the 990m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90m data (R2=0.709 and RMSE=2.702K). © 2011 Elsevier Inc.


Miao T.,China Agricultural University | Miao T.,Beijing Research Center for Information Technology in Agriculture | Zhao C.,Beijing Research Center for Information Technology in Agriculture | Guo X.,Beijing Research Center for Information Technology in Agriculture | Lu S.,Beijing Research Center for Information Technology in Agriculture
Mathematical and Computer Modelling | Year: 2013

This paper presents a framework for geometric modeling and appearance shading of plant leaves. In order to produce 3D models with multiple shapes, the double silhouette-axis skeleton method and Laplacian editing technology are used in the proposed modeling approach which ensure the interactive freedom and the geometric accuracy. A novel biological shading model is adopted to simulate leaves' appearance, which can generate different leaf color drove by different biological parameters such as structure parameter, chlorophyll a+. b content, the carotenoids content, epidermal cells' oblateness and blade thickness. With this framework, users can generate 3D models of various plant leaves easily and quickly with realistic appearance. We believe that it is a general method for modeling and rendering of plant leaves. © 2011 Elsevier Ltd.


Ge W.,Heilongjiang Bayi Agricultural University | Zhao C.,Beijing Research Center for Information Technology in Agriculture | Zhao C.,Key Laboratory of Information Technology in Agriculture
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2014

Agricultural internet of things (Ag-IoT) is the highly integrated and comprehensive application of the new generation of information technology in agricultural field. Ag-IoT is playing an important leading role in the agricultural informationization of China. It has changed the traditional agricultural production mode, and it is also promoting the transformation from the traditional agriculture to intelligent and precision agriculture. The concept of Ag-IoT and its technical system framework were firstly introduced. Then the research status and advances of sensing technologies, communicating technologies and key application technologies used in Ag-IoT were reviewed in detail. The challenges and problems existing in the development of Ag-IoT in China were further analyzed. Based on the analysis, countermeasures for the applications and development of Ag-IoT of China in many aspects were proposed, such as research priorities, development layout, advancing directions, application models and mechanisms for sustainable development.


Yang G.-J.,Beijing Research Center for Information Technology in Agriculture | Yang G.-J.,University of Chinese Academy of Sciences | Zhao C.-J.,Beijing Research Center for Information Technology in Agriculture | Huang W.-J.,Beijing Research Center for Information Technology in Agriculture | Wang J.-H.,Beijing Research Center for Information Technology in Agriculture
Hydrology and Earth System Sciences | Year: 2011

Soil moisture links the hydrologic cycle and the energy budget of land surfaces by regulating latent heat fluxes. An accurate assessment of the spatial and temporal variation of soil moisture is important to the study of surface biogeophysical processes. Although remote sensing has proven to be one of the most powerful tools for obtaining land surface parameters, no effective methodology yet exists for in situ soil moisture measurement based on a Bidirectional Reflectance Distribution Function (BRDF) model, such as the Hapke model. To retrieve and analyze soil moisture, this study applied the soil water parametric (SWAP)-Hapke model, which introduced the equivalent water thickness of soil, to ground multi-angular and hyperspectral observations coupled with, Powell-Ant Colony Algorithm methods. The inverted soil moisture data resulting from our method coincided with in situ measurements (R 2 Combining double low line 0.867, RMSE Combining double low line 0.813) based on three selected bands (672 nm, 866 nm, 2209 nm). It proved that the extended Hapke model can be used to estimate soil moisture with high accuracy based on the field multi-angle and multispectral remote sensing data. © 2011 Author(s).


Zhang J.-C.,Beijing Research Center for Information Technology in Agriculture | Zhang J.-C.,University of South Florida | Zhang J.-C.,Zhejiang University | Pu R.-L.,University of South Florida | And 5 more authors.
Computers and Electronics in Agriculture | Year: 2012

Powdery mildew (Blumeria graminis) is one of the most destructive diseases, which has a significant impact on the production of winter wheat. Detecting powdery mildew via spectral measurement and analysis is a possible alternative to traditional methods in obtaining the spatial distribution information of the disease. In this study, hyperspectral reflectances of normal and powdery mildew infected leaves were measured with a spectroradiometer in a laboratory. A total of 32 spectral features (SFs) were extracted from the lab spectra and examined through a correlation analysis and an independent t-test associated with the disease severity. Two regression models: multivariate linear regression (MLR) and partial least square regression (PLSR) were developed for estimating the disease severity of powdery mildew. In addition, the fisher linear discriminant analysis (FLDA) was also adopted for discriminating the three healthy levels (normal, slightly-damaged and heavily-damaged) of powdery mildew with the extracted SFs. The experimental results indicated that (1) most SFs showed a clear response to powdery mildew; (2) for estimating the disease severity with SFs, the PLSR model outperformed the MLR model, with a relative root mean square error (RMSE) of 0.23 and a coefficient of determination (R 2) of 0.80 when using seven components; (3) for discrimination analysis, a higher accuracy was produced for the heavily-damaged leaves by FLDA with both producer's and user's accuracies over 90%; (4) the selected broad-band SFs revealed a great potential in estimating the disease severity and discriminating severity levels. The results imply that multispectral remote sensing is a cost effective method in the detection and mapping of powdery mildew. © 2012 Elsevier B.V..


Zhao C.,Beijing Research Center for Information Technology in Agriculture | Zhao C.,Key Laboratory of Agri informatics
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2014

Agriculture is one of the most important and popular fields of remote sensing applications. The purpose of this paper is to review the advances of research and application in remote sensing for agriculture in the world. The review includes following six main aspects: cropland radiative transfer mechanism and remote sensing inversion of crop parameters, remote sensing classification and identification of crops, cropland nutrient and variable fertilization techniques, crop yield and quality perdition, agricultural disaster monitoring and forecasting, and spatial decision-making support system for agricultural remote sensing monitoring. Finally, the key directions needed more attention and technical breakthrough are figured out according to the current status and trends of agricultural remote sensing techniques. ©, 2014, Chinese Society of Agricultural Machinery. All right reserved.


Patent
Beijing Research Center For Information Technology In Agriculture | Date: 2011-10-13

Disclosed are a remote measurement system and method for pesticide fog distribution and drifting tendency in aerial pesticide application, which relate to the technical field of hazardous substance monitoring. The system comprises: a collection module, used for collecting infrared radiation in a detected area, and enabling the infrared radiation to be incident on an optical module; the optical module, used for obtaining, according to the incident infrared radiation, an infrared imaging spectrum in the detected area where a pesticide fog cloud cluster is distributed, and sending the infrared imaging spectrum to a processing module; the processing module, used for analyzing the infrared imaging spectrum, identifying the pesticide fog cloud, obtaining a concentration image of the pesticide fog cloud through inversion, and predicting a drifting tendency of the pesticide fog according to the concentration image. The system and method of the present invention can comprehensively reflect the drifting condition of the pesticide fog in the air in real time, and can obtain the concentration and components of the pesticide fog. The method and system improve the pesticide application efficiency, and prevent damages on the environment and residential areas while saving the cost.


Patent
Beijing Research Center For Information Technology In Agriculture | Date: 2013-09-25

In a device for producing quantitative-diameter spray droplets of pesticide, a driving motor is controlled by a control center, the driving motor drives a lead screw, the lead screw drives a sliding device to achieve a designated accurate position on a guide track, and a piston moves slowly along with the sliding device to extrude chemicals in a droplet generator quantitatively, the chemicals extruded by the piston through the motion in a droplet cavity forms small single droplets through a guide pipe, the droplets with specific particle size are further generated by precisely controlling the extrusion amount of the chemicals, so that the device can be widely used for studies about diffusion of the droplets evaporation property of the single droplets and the like tested by water-sensitive paper, as well as tests of the properties of the pesticide, and further has broad application prospects.

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