Artificial neural network modelling of common lambsquarters biomass production response to corn population and...

This study shows the ability of Artificial Neural Network (ANN) technology to be used for the prediction of the correlation between common lambsquarters (Chenopodium album L.) population, corn (Zea mays L.) population and planting pattern in different days after planting (as inputs) with common lambsquarters biomass production (as output). The number of patterns used in this study was 60 which were randomly divided into 45 and 15 data sets for training and testing the neural network, respectively. The results showed that a very good performance of the neural network is achieved. Some explanation of the predicted results is given. The multi layer perceptrons with training algorithm of backpropagation (BP) was the best one for creating nonlinear mapping between input and output parameters. The mean training of root mean square error (RMSE) was equal to 0.0156. ANN model predicted the common lambsquarters biomass with maximum RMSE, t-value, average prediction error and correlation coefficient of 0.0091, 0.985, 2.6% and 0.989, respectively. The ANN model, predicted common lambsquarters biomass within +/- 5% of the measured biomass for 59.8% of the samples indicates that the ANN can potentially be used to estimate plant biomass. Adjusting ANN parameters such as learning rate, momentum, number of patterns and number of hidden nodes/layers affected the accuracy of biomass production predictions.