Notice of RetractionModeling daily stem water content by artificial neural network

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.

The purpose of this paper was to model the daily stem water content with neural network. The output voltage of stem water content sensor changed as time series. In order to ensure the accuracy of the model, coefficients sc and eg must be adjusted with the RBF NN input vector changed. The dimensions of input vectors were grouped from 2, 4, 5 separately. After being grouped, observed data were input to the MATLAB neural network toolbox. To identify all parameters in the model, another set of observed data were used for testing the model. The result shows that the predicted values agreed well with the observed ones. The method of modeling daily stem water with neural network was available. It also indicated that daily stem water content model was not the better with the larger observed data dimensions. Since being measured hourly, the changes of daily stem water content has close correlation with the observed values in 2 hours. It will produce important basis for the precise irrigation system and significantly reduce the logging data storage capacity, greatly improve the computing speed.