Application of artificial neural network for wheat yield forecasting

A given model of yield forecasting using an artificial neural network connects the wheat crop with the amount of productive moisture in the soil, soil fertility, weather, and factors in the presence of pests, diseases, and weeds. The difficulty of creating a yield forecast system is in the correct choice of predictors that have the greatest impact on yield. To build the model, moisture in the 100 cm layer of the soil, the content of nitrogen, phosphorus, humus, and soil acidity in the soil were used as input parameters. The amount of precipitation over 4 months, the average air temperature for the same period, as well as the presence of diseases, pests, and weeds were also taken into consideration. Data on 13 districts of the North Kazakhstan region in the period from 2008 to 2017 were used. The output parameter was the yield of spring wheat over the same time period. The relative importance of input variables in relation to the output variable was used to determine the weight values of input variables. An artificial neural network of error backpropagation was used as a method. The advantage of this method is that the quality of the forecast increases with a large amount of training data, as well as the ability to model nonlinear relationships between different data sources. After training the artificial neural network and obtaining predictive data, good results were achieved for predicting wheat yields (p=0.52, mean absolute error in percentage (MAPE)=12.02 %, root mean square error (RMSE)=3.368). Thus, it is assumed that the developed model for forecasting wheat yields based on data can be easily adapted for other crops and places and will allow the adoption of the right strategies to ensure food securit