A generalized regression neural network and its application for leaf wetness prediction to forecast plant disease

Abstract

The objective of this paper was the development of an optimal generalized regression neural network (GRNN) for leaf wetness prediction. The GRNN prediction results were compared to those obtained with the standard multiple linear regression (MLR). Leaf wetness, which is difficult to measure directly, has an important effect on the development of disease on plants. In this study, leaf wetness was predicted from micrometeorological factors (temperature, relative humidity, wind speed, solar radiation and precipitation). Simulations showed than the MLR provided an average absolute prediction error of 0.1300 for the training set and 0.1414 for the test set. The GRNN provided an average absolute prediction errors of 0.0491 and 0.0894 on the same data sets, respectively. This error is very low since the leaf wetness initially varies between 0 and 1. The optimized GRNN, therefore, outperformed the MLR in terms of the prediction accuracy. However, the GRNN required more computational time than the MLR. The main disadvantage of the MLR is that it assumes a linear relationship between the feature to be predicted and the measured features. The GRNN automatically extracts the appropriate regression model (linear or nonlinear) from the data.