Neural network models for predicting flowering and physiological maturity of soybean

Date

1994

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

American Society of Agricultural and Biological Engineers

Type

Article

Peer reviewed

Yes

Abstract

It is important for farmers to know when various plant development stages occur for making appropriate and timely crop management decisions. Although computer simulation models have been developed to simulate plant growth and development, these models have not always been very accurate in predicting plant development for a wide range of environmental conditions. The objective of this study was to develop a neural network model to predict flowering and physiological maturity for soybean (Glycine max L. Merr.). An artificial neural network is a computer software system consisting of various simple and highly interconnected processing 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 complex data bases. For this study, field-observed flowering dates for the cultivar Bragg from experimental studies conducted in Gainesville and Quincy, Florida, and Clayton, North Carolina, were used. Inputs considered for the neural network model were daily maximum and minimum air temperature, photoperiod, and days after planting or days after flowering. The data sets were split into training sets to develop the models and independent data sets to test the models. The average relative error 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 prediction 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.

Description

Keywords

Citation

Elizondo, D.A., McClendon, R.W. and Hoogenboom, G. (1994) Neural network models for predicting flowering and physiological maturity of soybean. Transactions of the ASAE. 37(3), pp. 981-988

Rights

Research Institute

Institute of Artificial Intelligence (IAI)