遥感和病菌孢子捕捉技术在小麦白粉病监测中的应用分析-application and analysis of remote sensing and pathogen spore capture technology in monitoring wheat powdery mildew.docxVIP

AbstractWheat powdery mildew is one of the important diseases on wheat production. In this study, hyperspectral and unmanned aerial vehicle remote sensing technology was used to detect wheat powdery mildew at different planting densities. The losses on yield, 1000-kernel weight and crude protein content caused by the disease and the estimations on them by using remote sensing technology were also studied. Two 7-day volumetric spore samplers were used to monitor the dynamics in concentrations of Blumeria graminis f. sp. triciti (Bgt) conidia and their relationship to local weather conditions was analyzed. Disease prediction models based on the concentration of Bgt spores and meteorological factors were constructed. The concentrations of Bgt spores in the air estimated by microscopy on tapes and those determined with the Real-time PCR assay were compared. The main results are as follows.Diease indexes of powdery mildew in flowering, early and late filling stages have significant negative correlations with red edge slope (dλred) and the area of the red edge peak (SDr) at two planting densities. Only SAVI and DVI were significantly correlated with disease indexes in all three growth stages for both planting densities. Spectrum parameters got from the plots at nomal planting density have more significant correlations than those at 1/2 nomal planting density. According to the results of correlation analyses, several spectrum parameters (NIR, dλred, SDr, DVI SAVI) were choosed to construct disease detection models for both density levels at different gowth stages. Moreover, there were no significant differences in slope and intercept of the constructed models between two density levels, data at both planting densities were gathered up to construct new disease detection models. RED, RVI in early filling stage, RED, NDVI in flowering stage and RED, RVI in flowering stage were used to construct the loss estimation models of wheat yield, 1000-kernel weight and crude protein