Evaluation of the Ability of LVQ4 Artificial Neural Network Model to Predictthe Spatial Distribution Pattern of Sitona humeralis (Col: Curculionidae) in the alfalfa field in Marvdasht

In this research, a learning vector quantization neural network (LVQ) model was developed to predict the spatial distribution of Sitona humeralis in Marvdasht. This method was evaluated on data of pest density from alfalfa field. Pest density assessments were performed following a 10 m × 10 m grid pattern on the field and a total of 100 sampling units on field. Some statistical tests, such as means comparison, variance and statistical distribution were performed between the observed points samples data and the estimated pest values to evaluate the performance of prediction of pest distribution. The Results showed that in training and test phase, there were no significant differences, with the confidence level of 95%, between the statistical parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated pest density. The results suggest that learning vector quantization (LVQ4) neural network can learn pest density model precisely. In addition the results also indicated that trained LVQ4 neural network had a high capability (88%) in predicting pest density for non-sampled points. The technique showed that the LVQNN could predict and map the spatial distribution of Sitona humeralis. The map showed that the pest has aggregation distribution so there is possibility potential for using site-specific pest control on this field.