Entity

Time filter

Source Type


Zeng Q.,South China Agricultural University | Zhu T.,South China Agricultural University | Zhuang X.,South China Agricultural University | Zheng M.,South China Agricultural University | And 3 more authors.
Multimedia Tools and Applications | Year: 2015

Plant species identification is one of the most important research branches of botanical science. In this paper, a novel shape descriptor, namely Periodic Wavelet Descriptor (PWD) of plant leaf, is firstly presented. Then based on the PWDs of the leaves of different plant species, we constructed a database of PWDs. At last, a Back Propagation Neural Network (BPNN) is trained to fulfill the experiment of plant species identification. The experimental results show that the proposed algorithm combined the PWD of plant leaf with BPNN is effective with a correct identification rate about 90 %. © 2015 Springer Science+Business Media New York Source


Wang J.,South China Agricultural University | Ge A.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Li C.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Wang W.,South China Agricultural University | Hu Y.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation
2015 5th International Workshop on Computer Science and Engineering: Information Processing and Control Engineering, WCSE 2015-IPCE | Year: 2015

The traditional evaluation methods for land consolidation potential mainly depend on the experts' experience, statistics computations or subjective adjustments. There usually exists some bias in the results. So, the computer technology has been essential. In this study, an intelligent evaluation system based on Fuzzy Decision Tree is established, which can be deal with numerical data, discrete data and symbol data. When the land original data is input, its potential to be developed will be output by this new model. It is more helpful for authority to give out the objective proof for decision making. The project of Shunde's land consolidation provides the data support. The conventional evaluation results are compared. They are consistent roughly. The new model is more automatic and intelligent. Source


Wang J.,South China Agricultural University | Hu Y.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Ren X.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Wang W.,South China Agricultural University
2015 5th International Workshop on Computer Science and Engineering: Information Processing and Control Engineering, WCSE 2015-IPCE | Year: 2015

In land consolidation, it is very important to construct an effective index system. Land data is characteristic of big volume, complex varieties and more indexes. We need select a group of good index for some goals of land consolidation according to concrete demand. In this paper, Fuzzy Integrals is adopted to finish the feature selection. Fuzzy Integral is a kind of infusion tool based on fuzzy measure which can describe the importance of each feature or feature subset. Some researchers can obtain the optimal solution for Fuzzy measure using soft computing tools. When the Fuzzy integrals can be transformed to a linear equation, L1-norm regularization method is applied to solve the linear equation system and find a solution with the fewest nonzero values for fuzzy measure. The solution with the fewest nonzero can show the degree of contribution of some features or their combinations for decision. This method provides a quick and optimal way to determine the land index system for preparing the following land research. Source


Zhao Y.,South China Agricultural University | Zhao Y.,Guangdong Province Key Laboratory of Land Use and Consolidation | Zhao Y.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Zhang X.,Sun Yat Sen University | And 7 more authors.
Journal of Information and Computational Science | Year: 2014

Because Cellular Automata (CA) is a dynamic system with inherent parallelism, many studies are focused on mapping CA to the parallel system in order to obtain high performance computing capability, such as using clusters, supercomputers and networks of computers. But the application of these systems are too expensive and difficult to use on the occasions which need convenient computing. Recent developments in the General Purpose GPU can meet the desktop computing challenge, which have high performance at low cost. This paper presents a general-purpose approach to accelerate the CA in geographical domain in case of spatial simulation. A series of experiments are launched to test the performance of proposed method. Finally, the experimental results indicate the approach in this paper can obtain high performance and computational performance data using GPU based accelerating method are fifty to sixty times faster than the identical algorithms using CPU in test environment. It is worthy of (1) reducing the communication cost between GPU and CPU is crucial when visualization and processing are equally important in real time simulation and (2) improving the parallel capability in the CA functions is essential using GPU Programming. © 2014 Binary Information Press Source


Wang J.,South China Agricultural University | Wang J.,Guangdong Province Key Laboratory of Land Use and Consolidation | Wang J.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Ge A.,South China Agricultural University | And 10 more authors.
Solid Earth | Year: 2015

Traditionally, potential evaluation methods for farmland consolidation have depended mainly on the experts' experiences, statistical computations or subjective adjustments. Some biases usually exist in the results. Thus, computer-aided technology has become essential. In this study, an intelligent evaluation system based on a fuzzy decision tree was established, and this system can deal with numerical data, discrete data and symbolic data. When the original land data are input, the level of potential of the agricultural land for development will be output by this new model. The provision of objective proof for decision-making by authorities in rural management is helpful. Agricultural land data characteristically comprise large volumes, complex varieties and more indexes. In land consolidation, it is very important to construct an effective index system. A group of indexes need to be selected for land consolidation. In this article, a fuzzy measure was adopted to accomplish the selection of specific features. A fuzzy integral based on a fuzzy measure is a type of fusion tool. The optimal solution with the fewest non-zero elements was obtained for the fuzzy measure by solving a fuzzy integral. This algorithm provides a quick and optimal way to identify the land-index system when preparing to conduct land consolidation. This new research was applied to Shunde's "Three Old" consolidation project which provides the data. Our estimation system was compared with a conventional evaluation system that is still accepted by the public. Our results prove to be consistent, and the new model is more automatic and intelligent. The results of this estimation system are significant for informing decision-making in land consolidation. © Author(s) 2015. Source

Discover hidden collaborations