Detection of peroxidase activity in potato leaves under stress of
late blight using hyperspectral imaging
1. College of Mechanical and Electronic Engineering, Northwest A＆F University, Yangling, Shaanxi 712100, China; 2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Northwest A＆F University, Yangling, Shaanxi 712100, China
Abstract：To solve the difficulty of detecting potato late blight in real time, a prediction model was proposed based on XLWPLS model to predict the correlation between hyperspectral information of potato late blight leaves and POD activity of peroxidase. To reduce the dimension of spectral data and improve the operation speed of model, the prediction model was established by combining the continuous projection algorithm of SPA and the load coefficient method of XLW to select the characteristic wavelengths. The hyperspectral information and the peroxidase (POD) activity of potato leaves at different infection periods of 0, 24, 48, 72 and 96 h were measured. The spectral reflectance curves of samples were extracted by ENVI software and combined with various chemometrics methods to establish the association prediction model of hyperspectral information and peroxidase activity of potato late blight leaves. The results show that the LSSVM prediction model based on the full spectrum information has good prediction effects with RC of 0.916, RMSEC of 19.539 U·（g·min）-1 for calibration sets, RP of 0.932 and RMSEP of 14.966 U·（g·min）-1 for prediction sets. The XLWPLS model has the best prediction effects with RC of 0.870, RMSEC of 37.969 U·（g·min）-1， RP of 0.892 and RMSEP of 28.922 U·（g·min）-1. It is feasible to use realtime hyperspectral technology to detect potato late blight.
2018, Vol. 39 
