Highspectral prediction of early blight in processing tomato based on Gram-Schmidt algorithm and GRNN

Accurate prediction of early blight in processing tomato is good for taking active prevention measures and reducing loss of production. The canopy spectrum of early blight in processing tomato was measured, and continuum removal and transformation were conducted over 380-760 nm to get characteristic parameters of band depth, band position, band width, slope and area, and to extract characteristic parameters of red valley, green peaks, red edge and corresponding band position from original spectrum. Then the components were extracted from characteristic parameters by Gram-Schmidt algorithm, and the components were taken as input variable of General Regression Nerve Net (GRNN) to predict severity of early blight in processing tomato. The results show that compared with multiple linear regression prediction mode and prediction mode of partial least squares method, combined model of Gram-Schmidt algorithm and GRNN is more precise with R2 of 0.843 and RMSE of 0.136, which indicates that the model can rapidly and accurately predict the severity of early blight in processing tomato.