Key Laboratory of Agricultural Information Acquisition Technology Beijing

Beijing, China

Key Laboratory of Agricultural Information Acquisition Technology Beijing

Beijing, China
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Xue L.,China Agricultural University | Xue L.,Beijing Union University | Xue L.,Key Laboratory of Agricultural Information Acquisition Technology Beijing | Zhao D.-J.,China Agricultural University | And 9 more authors.
Information Processing in Agriculture | Year: 2016

SIET (Self-referencing Ion Electrode Technique) provides a novel electrophysiological tool which can non-invasively measure the dynamic influxes and effluxes of ions caused by the diffusion along the concentration gradients in vivo. However, in this technique ion fluxes are converted to voltage signals using an ion selective microelectrode at a small amplitude of μV, which is easy to be interfered by the ambient noise. Hence, effective solutions to the suppression of noise and calibration of ion flux measurement system are very important for this method. A K+-selective microelectrode was constructed using liquid ion exchangers (LIX) to investigate ion transport over plant tissue. A standard concentration gradient which simulates plant living cells was produced by an electrode with a certain tip diameter, filled with a solution containing a known K+ concentration in 100 mmol/L. An ion diffusion simulation model was established. This model evaluated the performance of ion flux measurement system in accuracy and reliability by comparing the consistency of the measured value and the predicted curve. K+ fluxes were measured within 25 min at each measuring point of distance 10, 20, 30, 40, 50, 80, and 100 μm from the K+ source, respectively. It can be seen that the K+ fluxes changes little, which indicates that ion flux measurement system has a reliable stability. The study provides a theoretical basis for a new non-invasive ion flux measurement method creation and a new sensors design. © 2016 China Agricultural University


Li T.,China Agricultural University | Li T.,Key Laboratory of Agricultural Information Acquisition Technology Beijing | Wang Z.-Y.,China Agricultural University | Wang Z.-Y.,Key Laboratory of Agricultural Information Acquisition Technology Beijing | And 6 more authors.
Information Processing in Agriculture | Year: 2016

Bioelectrical signals can reflect physiological state of organs or tissues in plants and have a significant potential value in research of plant stress tolerance. In order to study the relationship between environment factors and electrical signals in plant, a portable multi-channel physiological signal acquisition system which relevant in plant physiology research was developed. Environment parameters and electrical signals can be measured in different channels by the acquisition system simultaneously and the measurement data will be displayed in an embedded integrated touch screen which is the system processing core. The system was validated to be stable and reliable after the calibration and repeated experiments of recording electrical signals in Helianthus annuus L. © 2016 China Agricultural University


Jia S.-Q.,China Agricultural University | Li H.-C.,Henan Agricultural University | An D.,China Agricultural University | An D.,Key Laboratory of Agricultural Information Acquisition Technology Beijing | Liu Z.-H.,Henan Agricultural University
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2017

Accurate division of heterotic group in maize can provide effective information on germplasm improvement, heterosis mode construction, and development of new varieties. The main methods used at present to divide maize heterotic group are pedigree, combining ability test, isozyme markers, and molecular markers. These methods are inferior because of their high cost and operational complexity. Some of them even have to destroy the seeds. This study explored novel method feasibility of near infrared (NIR) spectroscopy in quick nondestructive heterotic grouping of maize. The near infrared diffuse reflection spectra of maize seeds were collected and preprocessed using smoothing, first derivative, and vector normalization. The features of these spectra were extracted through principal component analysis (PCA). Twelve Chinese maize inbred line samples were selected, including six leading inbred lines (Part A) and six excellent self-selection lines (Part B). Part A was divided into three groups with NIR spectroscopy, namely, A1 (Zheng58) and A2 (Ye478), A3 (Chang7-2) and A4 (Huangzaosi), and A5 (Mo17) and A6 (SiF1). This division was in accordance with pedigree analysis. Part B was also divided into three groups by NIR spectroscopy: B1 and B2, B3 and B4, as well as B5 and B6. This division conforms to clustering analysis based on SSR molecular marker. These processes confirm that NIR spectroscopy is a convenient, highly efficient, and feasible heterotic grouping method of maize. © 2017, Peking University Press. All right reserved.


Zhao S.-Y.,China Agricultural University | Ran H.,China Agricultural University | Jin Z.-X.,China Agricultural University | Cui Y.-J.,China Agricultural University | And 3 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2017

This paper focus on the effects of a high strength and high efficiency near infrared light source on the identification of corn hybrids under different light source voltage and different distances from the light source to the spectrograph based on the near infrared transmission(NIT) spectroscopy (wavelength range 908.1~1 677.2 nm) with the Nonghua 101 corn seeds harvested from Hainan province in 2009 as the research object. After the first order derivative and vector normalization of the spectra, the spectral characteristics are extracted using principal component analysis (PCA) and orthogonal linear discriminant analysis (OLDA) before the establishment of the model using support vector machine (SVM). Then the recognition rate under different experimental conditions is caculated. The results show that the lower voltage source or a larger distance from the spectrometer to the light source causes lower light intensity resulting to the spectrum curve with more burrs and the lower recognition rate. By increasing the voltage or decrease the distance from the light source to the spectrometer, the spectral curve becomes relatively smooth, and the recognition rate is significantly increased, indicating that the rate of correct identification of the model can be enhanced by increasing the light intensity within a certain range. © 2017, Peking University Press. All right reserved.


An D.,China Agricultural University | An D.,Modern Precision Agriculture System Integration Research Key Laboratory of Ministry of Education | Cui Y.,China Agricultural University | Liu X.,China Agricultural University | And 8 more authors.
PLoS ONE | Year: 2016

The effects of varieties, producing areas, ears, and ear positions of maize on near-infrared (NIR) spectra were investigated to determine the factors causing the differences in NIR fingerprints of maize varieties. A total of 130 inbred lines were grown in two regions in China, and 12,350 kernel samples were analyzed through NIR spectroscopy. Spectral differences among varieties, producing areas, ears, and ear positions were determined and compared on the basis of pretreated spectra. The bands at 1300-1470, 1768-1949, 2010-2064, and 2235-2311 nm were mainly affected by the producing area. Band selection and principal component analysis were applied to improve the influence of variety on NIR spectra by processing the pretreated spectra. The degrees of the influence of varieties, producing areas, ears, and ear positions were calculated, and the percentages of the influence of varieties, producing areas, ears, and ear positions were 45.40%, 42.66%, 8.22%, and 3.72%, respectively. Therefore, genetic differences among maize inbred lines are the main factors accounted for NIR spectral differences. Producing area is a secondary factor. These results could provide a reference for researchers who authenticate varieties, perform geographical origin traceabilities, and conduct maize seed breeding. © 2016 An et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Mu W.,China Agricultural University | Mu W.,Key Laboratory of Agricultural Information Acquisition Technology Beijing | Yan Z.,China Agricultural University | Gavrila S.P.,University of Galati | And 4 more authors.
Journal of Environmental Protection and Ecology | Year: 2015

Greenhouse grape cultivation (GGC) has been an important part in grape production in China, and the ecological impact of the production system is vital for its sustainable development. This paper aims to evaluate the net carbon emissions and the overall impact on atmosphere of GGC and open-feld grape cultivation (OGC) systems. A methodology of full carbon cycle analysis was adopted, and the net primary production, net ecosystem productivity and net carbon fux were chosen as the main indexes. The data were acquired through feld investigations and chemical experiments. The results showed that both systems were the carbon source. The net carbon fux were 9.77 mg ha-1 year-1 and 1.17 mg ha-1 year-1. Compared to the OGC system, the GGC system is a bigger carbon source, however the carbon sink ability was improved and the NEP was increased by 2.18 mg ha-1 year-1. Minimisation the use of high carbon material in greenhouse building is a practical solution to reduction of carbon emission.


Jia S.,China Agricultural University | Jia S.,Key Laboratory of Agricultural Information Acquisition Technology Beijing | An D.,China Agricultural University | An D.,Modern Precision Agriculture System Integration Research Key Laboratory of Ministry of Education | And 7 more authors.
Journal of Cereal Science | Year: 2015

Near infrared spectroscopy (NIRS) has been used to identify variety authenticity of bare maize kernels. However, maize seeds are coated with seed coating agents in practice. Therefore it is of great significance to investigate the feasibility of identifying coated maize kernels by NIRS. This study employed NIRS to quickly determine the variety to which a sample of coated seed belonged. The NIR spectra of clean and coated seeds were obtained using MPA spectrometry in diffuse reflectance mode by three methods. Influence of seed coating agent on NIR spectra was discussed based on spectra collected from whole single seed by method 1. In method 2, to eliminate the influence of seed coat, the seed coat agent on the back side of the seed was polished away. In method 3, clean and coated seeds were cut open along the crease on the embryo side, and the sections were scanned by the spectrometer, so as to acquire information on the seed, and to avoid the influence of the seed coating agent. Then, support vector machine (SVM), soft independent modeling of class analogy (SIMCA), and biomimetic pattern recognition (BPR) were employed to establish the identification model for four maize varieties. Performance of variety models based on spectra measured by method 1 and 2 were poor. In method 3, the SIMCA model showed better performance than SVM and BPR models, and achieved an accuracy rate of 97.5%. The results demonstrated the feasibility of utilizing NIRS and chemometrics, as an objective and rapid method for the identification of maize seeds with seed coating agents. © 2014 Elsevier Ltd.


Xiong B.,China Agricultural University | Wen H.,China Agricultural University | Liu X.,China Agricultural University | Li X.,China Agricultural University | And 3 more authors.
Journal of Food, Agriculture and Environment | Year: 2013

This research designed and developed a video database of crop and its retrieval system called CVRS. The system is a web based tool that helps farmers retrieve crop videos which enables users to select the video fragment they want without watching whole video. To demonstrate the system's potential and effectiveness, we used the cucumber diseases and pests prevention and control video as an example. For more accurate retrieval results, we adopt the timeline-based-segmentation and the notation-based-indexing as main methods. This system would improve the accuracy and professional standard of crop video retrieval.


Liu S.,Guangdong Ocean University | Liu S.,Key Laboratory of Agricultural Information Acquisition Technology Beijing | Liu S.,China Agricultural University | Xu L.,Guangdong Ocean University | And 4 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2014

Water quality regulation is one of the most important tasks in intensive aquaculture management. Grasping the trend of the dissolved oxygen concentration timely and accurately and regulating water quality dynamics are the key for healthy growth in the non-stress environment of aquatic products in order to solve the low prediction accuracy, inferior capability of dynamic learning, online updates, and high computational complexity of the traditional online forecasting methods for water quality in intensive aquaculture. The online prediction model of dissolved oxygen content in intensive aquaculture eriocheir sinensis cultures was introduced, which was based on the least squares support vector machine (LSSVR) with time series similar data. The time series data collected online was segmented clustered using a feature points segmented time warping distance algorithm. The subsequence data sets reduced the size and optimized the LSSVR models training process, achieving multiple LSSVR models online modeling, and segmented memory and storage. According to the forecast data sequence and LSSVR sub-model similarity, it adaptively chose the optimal sub-model to get the predicted output. The online model was used for the prediction of the dissolved oxygen changing in high-density eriocheir sinensis culture ponds during July 21, 2012 to July 31, 2012 in Yixing City, Jiangsu Province, China. Experimental results showed that the proposed prediction model of FPSTWD-LSSVR had a better prediction effect than the FPSTWD-LSSVR, ILSSVR, SONB-LSSVR, or off-line LSSVR algorithms. Under the same experimental conditions, the relative mean absolute percentage error (MAPE), maximum relative error (Emax), relative root mean square error (RRMSE), and the running time differences between the FPSTWD-LSSVR and ILSSVR models were 47.93%, 43.47%%, 30.91%, and 5.16 s in the test period respectively. The relative MAPE, Emax, RRMSE, and the running time differences between the FPSTWD-LSSVR and SONB-LSSVR models were 39.99%, 33.43%, 22.40%, and 2.74 s in the test period respectively. It is obvious that FPSTWD-LSSVR is more accurate than ILSSVR and SONB-LSSVR. The relative MAPE, Emax, RRMSE and the running time differences between the FPSTWD-LSSVR and off-line LSSVR models were 16.14%, 9.03%, 8.41%, and 11.36 s in the test period respectively. The lower sample number, which cannot cover all types of characteristic in time series data, probably caused the prediction performance of FPSTWD-LSSVR to be slightly lower than the off-line LSSVR model. Overall, the online prediction model has a low computational complexity, fast convergence rate, high online prediction accuracy, and strong generalization ability. It is an effective online prediction method for the dissolved oxygen controlling in the high density eriocheir sinensis culture, and provides the basis of decisions for controlling water quality, setting the aquaculture water plan, and reducing the risk of cultivation.


Wang X.,China Agricultural University | Wen H.,China Agricultural University | Wen H.,Beijing Laboratory of Food Quality and Safety | Li X.,Beijing Laboratory of Food Quality and Safety | And 6 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2016

The occurrence of agricultural diseases has been one of the restrict factors of sustainable agricultural development for a long time, and agricultural diseases early warning technology gradually becomes a hot issue in China and abroad. Based on the literature review, the important meaning of agricultural diseases early warning technology for agricultural development was presented. This article expounded the characteristics and classification of agriculture diseases early warning firstly. And then a systematical analysis and discussion were carried on the key technologies for agricultural diseases early warning information acquisition, mainly summarizing the internet of things and sensor technology, 3S technology, spectrum technology and pathogenic microorganism examination technology. And also an introduction of agricultural diseases early warning information processing technologies was made, such as image processing technology, expert system of disease early warning and disease prediction technology. Finally, the conclusion of the whole article was obtained. The results indicated that the integration and combination of muti-technology would cover the whole agricultural diseases early warning area and get a higher accuracy of disease early warning; the acquisition of agricultural information was becoming precision and extensive; the early detection, diagnose and warning of agricultural diseases would be a new development direction; the agricultural disease early warning systems and equipment would be developed with lower cost; the real-time and online agricultural diseases automatic early warning will be an important research direction. © 2016, Chinese Society of Agricultural Machinery. All right reserved.

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