Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 62

Four statistical parameters are used for performance analysis of the developed FFBPNN models, namely R², RMSE, MAE, and the R_ratio. These parameters are calculated using the testing data for finding out to optimize neural network. The criteria for optimum neural network are minimum RMSE, minimum MAE (Should be optimally 0), and the value of coefficient of determination is nearer to 1. The R_ratio is used to explain the models underprediction or overprediction of the simulated yield values. R_ratio that is less than 1 indicates underestimation, R_ratio that is more than 1 indicates overestimation. The relative error for each data point was also calculated. Additionally, the simulated values were plotted against the observed values and tested the statistical significance of parameters of regression analysis.

To achieve this, training, validation, and test set were used to adjust the weights and biases, to stop the training process and for external prediction, respectively. Initially, 75% of samples are randomly selected for training, and the remaining 25% are used for testing to evaluate the model performance. The selected four statistical parameters were used for performance analysis of the developed FFBPNN models. These parameters were calculated using the testing data for finding out optimized neural network. The normalized output results (yields) from the ANN model are converted into original values at the end. Relative error between the targeted and neural network model predicted yield values. All relative errors of the model obtained are smaller than 10% except for two readings. 85% of the relative errors between predicted and observed values are even smaller than 10%.