Rohani, A., Makarian, H. (2011). Making Weed Management Maps by Artificial Neural Networks for Using in Precision Agriculture. , 1(2), -. doi: 10.22067/jam.v1i2.11355
A. Rohani; H. Makarian. "Making Weed Management Maps by Artificial Neural Networks for Using in Precision Agriculture". , 1, 2, 2011, -. doi: 10.22067/jam.v1i2.11355
Rohani, A., Makarian, H. (2011). 'Making Weed Management Maps by Artificial Neural Networks for Using in Precision Agriculture', , 1(2), pp. -. doi: 10.22067/jam.v1i2.11355
Rohani, A., Makarian, H. Making Weed Management Maps by Artificial Neural Networks for Using in Precision Agriculture. , 2011; 1(2): -. doi: 10.22067/jam.v1i2.11355
Making Weed Management Maps by Artificial Neural Networks for Using in Precision Agriculture
2Faculty of Agriculture, Shahrood University of Technology
Abstract
With the rise of new powerful statistical techniques and neural networks models, the development of predictive species distribution models has rapidly increased in ecology. In this research, a learning vector quantization (LVQ) and multi layer perceptron (MLP) neural network models have been employed to predict, classify and map the spatial distribution of A. repens L. density. This method was evaluated based 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 by two neural networks to evaluate the performance of the pattern recognition method. Results showed that in the training and test phases non significant different was observed between average, variance, statistical distribution in the observed and the estimated weed density by using LVQ neural network. While this comparisions was significant except statistical distribution by using MLP neural network. In addition, results indicated that trained LVQ neural network has a high capability in predicting weed density with recognition erorr less than 0.64 percent at unsampled points. While, MLP neural network recognition erorr was less than 14.6 percent at unsampled points. The maps showed that, patchy weed distribution offers large potential for using site-specific weed control on this field.
