Abstract

A new prediction model for the early warning of apple scab is proposed in this study. The method is based on artificial intelligence and time series prediction. The infection period of apple scab was evaluated as the time series prediction model instead of summation of wetness duration. Also, the relations of different measurements with apple scab infection time were analyzed. The important hours of duration were determined with the feature selection methods, such as Pearson’s correlation coefficients (PCC), Fisher’s linear discriminant analysis (FLDA) and an adaptive neuro-fuzzy classifier with linguistic hedges (ANFC_LH). The experimental dataset with selected features was classified by ANFC_LH, and predicted by an adaptive neural network (ANN) model. The proposed ANN model successfully predicts the apple scab infection time with 2 to 5% error rates compared to the traditional weather station predictions. The results show that the last 24-hour period is important to determine the apple scab infection at any time.

Key words: Apple scab (Venturia inaequalis), early warning, time series prediction, feature selection, artificial intelligence.

Abbreviation

PCC, Pearson's correlation criterion; FLDA, Fisher’s linear discriminant analysis; LWS, Lufft weather station; ANFC_LH, adaptive neuro-fuzzy classifier with linguistic hedge; ASD, apple scab degree; MLPN, multi-layer perceptron network;RMSE, root mean square error.