DocumentCode
3151292
Title
Notice of Retraction
Build on greenhouse roses disease prediction model based on genetic BP neural network
Author
Shu-jie Wang ; Hai-wei Niu ; Li-li Liu ; Li-na Yuan ; Yuan-yuan Sui
Author_Institution
Sch. of Biol. & Agric. Eng., Jilin Univ. Changchun, Changchun, China
fYear
2011
fDate
16-18 April 2011
Firstpage
3011
Lastpage
3014
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Through the application of genetic algorithms (genetic algorithm, simplified as GA) and BP(Back Propation) neural network, I built a prediction model of roses diseases, in which I choose six indicators as the input of network, they are the minimum temperature, maximum temperature, average temperature, minimum humidity, maximum humidity, average humidity in the greenhouse, then I choose three diseases index as the output of the network, they are rose powdery mildew, downy mildew, gray mold. Using Matlab platform to complete the model, the results show that: based on genetic algorithm and BP neural network model the diseases of roses can be predicted. The average relative error is about 6.37%, so it has good effect, and compared with the traditional BP network model, the results show that the model based on genetic algorithms and BP neural network is superior to the traditional BP network model, which have higher precision and stability.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Through the application of genetic algorithms (genetic algorithm, simplified as GA) and BP(Back Propation) neural network, I built a prediction model of roses diseases, in which I choose six indicators as the input of network, they are the minimum temperature, maximum temperature, average temperature, minimum humidity, maximum humidity, average humidity in the greenhouse, then I choose three diseases index as the output of the network, they are rose powdery mildew, downy mildew, gray mold. Using Matlab platform to complete the model, the results show that: based on genetic algorithm and BP neural network model the diseases of roses can be predicted. The average relative error is about 6.37%, so it has good effect, and compared with the traditional BP network model, the results show that the model based on genetic algorithms and BP neural network is superior to the traditional BP network model, which have higher precision and stability.
Keywords
agriculture; backpropagation; biology computing; botany; diseases; genetic algorithms; greenhouses; neural nets; Matlab platform; back propagation neural network; disease index; downy mildew; genetic BP neural network; genetic algorithms; gray mold; greenhouse roses disease prediction model; rose powdery mildew; Artificial neural networks; Biological system modeling; Diseases; Gallium; Genetic algorithms; Mathematical model; Predictive models; BP neural network; diseases; genetic algorithm; greenhouse roses; prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on
Conference_Location
XianNing
Print_ISBN
978-1-61284-458-9
Type
conf
DOI
10.1109/CECNET.2011.5768383
Filename
5768383
Link To Document