Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 61

The trained ANN model recognizes the functional relationship between input parameters and desired outputs. The network training started with a random initiation of weights and proceeds and optimizes an error function (RMSE) [72, 73]. The generated weights by neural network saves and also remembers this functional relationship for further calculations. Yield estimation models are developed at the regional scale for paddy crops in kharif season. The right time stopping of the training of neural network is called because early stopping is an important step to avoid over fitting. To achieve this, the training, validation, and test set data were used to adjust the weights of neuron and bias, to stop the training process, and for external prediction respectively. Initially, 75% of the samples are selected randomly for the training, and the remaining 25% are used for testing to evaluate the model performance.

The data of different parameters have wide range of values. For uniformity and also to avoid the confusion of learning algorithm, all the input data are normalized before input layer to represent 0 as minimum and 1 as maximum values. The output results (yields) are converted back to the similar unit by a denormalization procedure. Learning rate, number of hidden nodes, and training tolerance were adjusted. The initial selected number of hidden nodes was equal to inputs +1.