A neural network for setting target corn yields

Setting a realistic yield goal in each part of the field is one of the critical problems in precision agriculture. Factors affecting crop yields, such as soil, weather, and management, are so complex that traditional statistics cannot give accurate results. As an automatic learning tool, the artificial neural network (ANN) is an attractive alternative for processing the massive data set generated by precision farming production and research. A feed-forward, completely connected, back-propagation ANN was designed to approximate the nonlinear yield function relating corn yield to factors influencing yield. By stratified sampling based on rainfall, some of the data were excluded from the training set and used to verify the yield prediction accuracy of the ANN. The RMS error for 60 verification patterns was about 20%. After the ANN was developed and trained, three aspects of the input factors were investigated: (1) yield trends with 4 input factors, (2) interaction between nitrogen application rate and late July rainfall, and (3) optimization of the 15 input factors with a genetic algorithm to determine maximum yield. The model was then used on another field, and preliminary results of the latter study are given.