NEURAL-NETWORK MODELS FOR PREDICTING FLOWERING AND PHYSIOLOGICAL MATURITY OF SOYBEAN

It is important for farmers to know when various plant development sta ges occur for making appropriate and timely crop management decisions. Although computer simulation models have been developed to simulate p lant growth and development, these models have not always been very ac curate in predicting plant development for a wide range of environment al conditions. The objective of this study was to develop a neural net work model to predict flowering and physiological maturity for soybean (Glycine max L. Merr.). An artificial neural network is a computer so ftware system consisting of various simple and highly interconnected p rocessing elements similar to the neuron structure found in the human brain. A neural network model was used because it has the capabilities to identify relationships between variables of rather large and compl ex data bases. For this study, field-observed flowering dates for the cultivar 'Bragg' from experimental studies conducted in Gainesville an d Quincy, Florida, and Clayton, North Carolina, were used Inputs consi dered for the neural network model were daily maximum and minimum air temperature, photoperiod, and days after planting or days after flower ing. The data sets were split into training sets to develop the models and independent data sets to test the models. The average relative er ror of the test data sets for date of flowering prediction was + 0.143 days (n = 21, R2 = 0.987) and for date of physiological maturity pred iction was + 2.19 days (n = 21, R2 = 0.950). It can be concluded from this study that the use of neural network models to predict flowering and physiological maturity dates is promising and needs to be explored further.