Plant Electrophysiological Parameters Represent Leaf Intracellular Water-Nutrient Metabolism and Immunoregulations in During Infection.

Although () is vital in agricultural production and vulnerable to the pathogen , the intracellular water-nutrient metabolism and immunoregulation of infection in leaves remain unclear. This study aimed to analyze the responsive mechanisms of -infected using rapid detection technology. Six soil groups planted with Yangtze No. 5 were inoculated with varying concentrations (from 0 to 10 × 10 spores/mL). The results showed that at the highest infection concentration (PWB5, 10 × 10 spores/mL) of leaves, the plant electrophysiological parameters showed the intracellular water-holding capacity (IWHC), the intracellular water use efficiency (IWUE), and the intracellular water translocation rate (IWTR) declined by 41.99-68.86%. The unit for translocation of nutrients (UNF) increased by 52.83%, whereas the nutrient translocation rate (NTR), the nutrient translocation capacity (NTC), the nutrient active translocation (NAT) value, and the nutrient active translocation capacity (NAC) decreased by 52.40-77.68%. The cellular energy metabolism decreased with worsening infection, in which the units for cellular energy metabolism (∆G) and cellular energy metabolism (∆G) of the leaves decreased by 44.21% and 78.14% in PWB5, respectively. Typically, based on distribution of B-type dielectric substance transfer percentage (BPn), we found PWB4 (8 × 10 spores/mL) was the maximal immune response concentration, as evidenced by a maximal BPn (B-type dielectric substance transfer percentage based on resistance), with increasing lignin and cork deposition to enhance immunity, and a minimum BPn (B-type dielectric substance transfer percentage based on capacitive reactance), with a decreasing quantity of surface proteins in the leaves. This study suggests plant electrophysiological parameters could characterize intracellular water-nutrient metabolism and immunoregulation of leaves under various infection concentrations, offering a dynamic detection method for agricultural disease management.