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

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

Automatic clustering of data from sampling and evaluating of neuro-fuzzy network forestimatinge the distribution of Bemisia. tabaci (Hem.:Aleyrodidae)

In this study, Neuro Fuzzy network was used to estimate the spatial distribution of Bemisia tabaci in a cucumber field in Behbahan. Pest density assessments were performed  based on a 10 m × 10 m grid pattern pattern and a total of 100 sampling units in. In this method  latitude and longitude information was used the input data and output of method showed the number of pest. To determine the sensitivity of this method to different levels of the pest after collecting samples, automatic clustering method was used to determine the number of clusters Davies and Bouldin index was used to evaluae criterion. In order to finding the answer, Clustering Search Space Genetic Algorithm was used.Davies and Bouldin index (0.46) showed that the data should be divided into three clusters. Results indicated average, variance, statistical distribution and also coefficient of determination in the observed and the estimated Bemisia tabaci density were not significantly different.Our map showed that patchy pest distribution offers large potential for using site-specific pest control on this field

ENGINEERING-BASED COPMUTER SIMULATION FOR MODELING GREENHOUSE WHITEFLY POPULATION GROWTH

Abstract- Mathematical simulation model is very suitable to understand insect dynamical systems. This system is nonlinear and it is hard to deal with by pure mathematical models only. It offers a possibility to recognize possible gaps in our knowledge. A simulation model of immature stages development of greenhouse whitefly Trialeurodes vaporariorum (westwood) population is presented. The parameters, variables, selection of data used for the model and the formulation of equations are discussed. A network form and a program listing are provided with technical details on modeling with SLAM II software. To validate model there were two data sets available to compare the model result with. The data came from experiments that were carried out in greenhouse environment. In first experiment there were followed and recorded the numbers of developmental stages of two sets of 106 and 115 whitefly eggs on tomato leaves daily. In second experiment there were followed and recorded the numbers of empty pupa of two sets of 100 and 115 whitefly eggs on tomato leaves in 80 and 90 days respectively. Numerical results and graphs from model and greenhouse condition are compared presented