Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN)

Late blight (LB) is one of the most aggressive tomato diseases in California. Accurately detecting the disease will increase the efficiency of properly controlling the disease infestations to ensure the crop production. In this study, we developed a method to spectrally predict late blight infections on tomatoes based on artificial neural network (ANN). The ANN was designed as a back-propagation (BP) neural network that used gradient-descent learning algorithm. Through comparing different network structures, we selected a 3-25-9-1 network structure. Two experimental samples, from field experiments and remotely sensed image data sets, were used to train the ANN to predict healthy and diseased tomato canopies with various infection stages for any given spectral wavelength (m) intervals. Results of discrete data indicated different levels of disease infestations. The correlation coefficients of prediction values and observed data were 0.99 and 0.82 for field data and remote sensing image data, respectively. In addition, we predicted the field data based on the remote sensing image data and predicted the remote sensing image data with field data using the same network structure, and the results showed that the coefficient of determination was 0.62 and 0.66, respectively. Our study suggested an ANN with back-propagation training could be used in spectral prediction in the study.