叶绿素荧光成像技术的葡萄霜霉病早期检测

葡萄霜霉病对葡萄生产构成严重威胁,尽早防治是治理霜霉病的关键.为了对该病进行早期检测,以PCR检测获取的霜霉病相对生物量作为霜霉病侵染的依据,从暗适应—光适应—暗弛豫3个光合生理状态连续变化过程中,采集80个人工接种霜霉菌叶片和80个健康对照叶片连续6 d的叶绿素荧光图像.对比健康和接种叶片叶绿素荧光动力学曲线、参数图...

Early Detection of Plasmopara Viticola Infection in Grapevine Leaves Using Chlorophyll Fluorescence Imaging

Plasmopara viticola (P. viticola）infection poses a serious threat to grape production. Early prevention and treatment is essential to the control of P. viticola infection. In order to detect this disease early, the relative biomass of P. viticola detected by PCR as the basis of P. viticola infection, the chlorophyll fluorescence images of 80 grape leaves inoculated with P. viticola and 80 healthy control leaves were collected for 6 consecutive days from the three continuous changes of photosynthetic physiological state, namely dark adaptation, light adaptation and dark relaxation, using the relative biomass of downy fungus as the basis of P. viticola infection. The sensitivity of chlorophyll fluorescence parameters to downy mildew infection was evaluated by one-way analysis of variance (ANOVA). The optimal feature subset of chlorophyll fluorescence parameters extracted by feature selection strategies was input to machine learning classifiers to establish the early detection model of P. viticola infection. The results showed that with the increase of DPI, the degree of downy mildew infection was deepened, and the chlorophyll fluorescence dynamics curves and parameters of healthy and inoculated leaves were significantly different from 2DPI (p<0.01). Due to the infection, the photochemical quenching rate of inoculated leaves decreased (Rfd decreased), and the photosynthetic efficiency decreased (F_v/F_m decreased). Leaf vitality and photoprotection ability continued to decline (NPQ and qN decreased), and the light energy absorbed by leaves was more released in the form of fluorescence (F_t and F_m increased). BP neural network model using the feature subset (qN-L3, RFD-L2, NPQ-L1 and F_v/F_m-D1) optimized by the SFFS algorithm had the best detection accuracy, and the detection accuracy of healthy, and inoculated leaves at 3DPI was 83.75%. The average accuracy of the whole experiment period for 6 consecutive days reached 85.94%. These results provide a fast and accurate method for photosynthetic phenotype analysis and early detection of grape downy mildew.