Determination of Spatial Distribution Pattern of Alhagi pseudalhagi (M.B) Desv. Population Using Learning Vector Quantization Neural Network Model (LVQ4)

Document Type : Research Article

Authors

• A. Rohani
• H. makarian

10.22067/jpp.v27i1.22170

Abstract

Mapping of weeds distribution patterns for using in site-specific weed management has been favored by researchers. In this study, a learning vector quantization neural network (LVQ4) model was developed to predict and classify the spatial distribution patterns of Alhagi pseudalhagi. This method was evaluated on data of weed density counted at 550 points of a fallow field located in Faculty of Agriculture, Shahrood University of Technology, Semnan, Iran, in 2010. Some statistical tests, such as comparisions of the means, variance, statistical distribution as well as coefficient of determination in linear regression were used between the observed point sample data and the estimated weed seedling density surfaces to evaluate the performance of the pattern recognition method. Results showed that in training LVQ4, test and total phase P-value was greater than 0.7, 0.2 and 1.000 percent respectively, indicating that there was no significant (p

Keywords

• Patchy distribution
• Precision farming
• Classification
• Weed map

20.1001.1.20084749.1392.27.1.14.3

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