Using Spectral Reflectance to Estimate the Leaf Chlorophyll Content of Maize Inoculated With Arbuscular Mycorrhizal Fungi Under Water Stress - Sun, Jinhua; Yang, Liu; Yang, Xitian; Wei, Jie; Li, Lantao; Guo, Erhui; Kong, Yuhua (Frontiers in Plant Science, 2021)
The leaf chlorophyll content is an important indicator for the growth and photosynthesis of maize under water stress. The promotion of maize physiological growth by arbuscular mycorrhizal fungi (AMF) has been studied. However, studies of the effects of AMF on the leaf chlorophyll content of maize under water stress observed through spectral information is rare. In this study, a pot experiment was carried out to spectrally estimate the leaf chlorophyll content of maize subjected to different water stress durations (20 days, 35 days and 55 days), different water stress degrees (75%, 55% and 35% water supply) and two inoculation treatments (inoculation with Funneliformis mosseae and no inoculation). Three machine learning algorithms, including the back propagation (BP) method, least square support vector machine (LSSVM) and random forest (RF) method, were used to estimate the leaf chlorophyll content of maize. The results showed that AMF increased the leaf chlorophyll content, net photosynthetic rate (A), stomatal conductance (gs), transpiration rate (E) and water use efficiency (WUE) of maize but decreased the intercellular carbon dioxide concentration (Ci) and atmospheric vapor pressure deficit (VPD) of maize regardless of the water stress duration and degree. The first-order differential reflectance of maize leaves was more significantly correlated with the chlorophyll content of maize than the original spectral reflectance. The BP model performed best for the maize leaf chlorophyll content, with the largest R2 and smallest root mean square error values, regardless of stress duration. These results provide a reliable basis for the effective monitoring of the leaf chlorophyll content of maize under water stress.