Regression and Artificial Neural Network Modeling for the Prediction of Gray Leaf Spot of Maize

Abstract

Regression and artificial neural network (ANN) modeling approaches were combined to develop models to predict the severity of gray leaf spot of maize, caused by Cercospora zeae-maydis. In all, 329 cases consisting of environmental, cultural, and location-specific variables were collected for field plots in Iowa between 1998 and 2002. Disease severity on the ear leaf at the dough to dent plant growth stage was used as the response variable. Correlation and regression analyses were performed to select potentially useful predictor variables. Predictors from the best 9 of 80 regression models were used to develop ANN models. A random sample of 60% of the cases was used to train the networks, and 20% each for testing and validation. Model performance was evaluated based on coefficient of determination (R2) and mean square error (MSE) for the validation data set. The best models had R2 ranging from 0.70 to 0.75 and MSE ranging from 174.7 to 202.8. The most useful predictor variables were hours of daily temperatures between 22 and 30°C (85.50 to 230.50 h) and hours of nightly relative humidity ≥90% (122 to 330 h) for the period between growth stages V4 and V12, mean nightly temperature (65.26 to 76.56°C) for the period between growth stages V12 and R2, longitude (90.08 to 95.14°W), maize residue on the soil surface (0 to 100%), planting date (in day of the year; 112 to 182), and gray leaf spot resistance rating (2 to 7; based on a 1-to-9 scale, where 1 = most susceptible to 9 = most resistant).