Notice of RetractionClustering analysis on disease severity of wheat stripe rust based on SOM 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.

A SOM (Self-organizing Feature Maps) model was introduced to cluster and analysis on the disease severity of wheat stripe rust based on PHI (Pushbroom hyperspectral imager) data. By means of acquiring the spectral index data (SID) and spectral angle data (SAD) of the samples, combining with the samples´ spectral average reflectance data (ARD), three two-dimensional data matrixes were obtained as the inputs of SOM model. After iterative learning and self-organized clustering, the models´ outputs farthest approached to the reality in 3-dimensional severity space of wheat stripe rust. Then, with the net-trained, all data of the trial plot were simulated. The simulating results demonstrate that the division of wheat stripe rust severity is obviously. The whole trial spot was derived into four grades and the results are satisfactory.