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. Source
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. Source
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. Source
Jia S.-Q.,China Agricultural University |
Jia S.-Q.,Modern Precision Agriculture System Integration Research Key Laboratory of Ministry of Education |
Guo T.-T.,China Agricultural University |
Liu Z.,China Agricultural University |
And 7 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2014
It is generally accepted that near infrared reflectance spectroscopy (NIRS) can be used to identify variety authenticity of bare maize seeds. In practical, maize seeds are covered with seed coating agents. Therefore it's of huge significance to investigate the feasibility of identifying coated maize seeds by NIRS. This study employed NIRS to quickly determine the variety of coated maize seeds. Influence of seed coating agent on NIR spectra was discussed. The NIR spectra of coated maize seeds were obtained using an innovative method to avoid the impact of the seed coating agent. Coated seeds were cut open, and the sections were scanned by the spectrometer, so as to acquire the information of the seed itself. Then, support vector machine (SVM), soft independent modeling of class analogy (SIMCA), and biomimetic pattern recognition (BPR) was employed to establish the identification model for four maize varieties, and yield 93%, 95.8%, 98% average correct rate respectively. BPR model showed better performance than SVM and SIMCA models. The robustness of identification model was tested by seeds harvested from four regions and model showed good performance. Source
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. Source