Evaluation of LVQ4 artificial neural network model for Predicting spatial distribution pattern of Tuta absoluta in Ramhormoz, Iran

Abstract

In this research, a Learning Vector Quantization (LVQ) neural network model was developed to predict the spatial distribution of Tuta absoluta in tomato fields of the city of Ramhormoz, Iran. Pest density was assessed through 10 m × 10 m grid pattern on the field with a total of 100 sampling units. Some statistical tests, such as means comparison, variance and statistical distribution were performed between the sampling point data and the estimated pest values in order to evaluate the performance of prediction of pest distribution. In training and test phase, there was no significant difference in average, variance, statistical distribution and coefficient of determination at 95% confidence level. The results suggest that LVQ neural network can learn pest density model precisely and trained LVQ neural network high capability (88%) of predicting pest density for non-sampled points. The LVQNN successfully predicted and mapped the spatial distribution of Tuta absoluta whose aggregation distribution implied the possibility of using site-specific pest control in the field.

Keywords

• artificial neural network
• spatial distribution
• tomato leaf miner moth