Using Spectral Reflectance to Estimate the Leaf Chlorophyll Content of Maize Inoculated With Arbuscular Mycorrhizal Fungi Under Water Stress

Abstract

Leaf chlorophyll content is an important indicator of the growth and photosynthesis of maize under water stress. The promotion of maize physiological growth by (AMF) has been studied. However, studies of the effects of AMF on the leaf chlorophyll content of maize under water stress as observed through spectral information are rare. In this study, a pot experiment was carried out to spectrally estimate the leaf chlorophyll content of maize subjected to different durations (20, 35, and 55 days); degrees of water stress (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) of maize and atmospheric vapor pressure deficit (VPD) regardless of the water stress duration and degree. The first-order differential spectral data can better reflect the correlation between leaf chlorophyll content and spectrum of inoculated maize when compared with original spectral data. The BP model performed bestin modeling the maize leaf chlorophyll content, yielding the largest R2-values and smallest root mean square error (RMSE) 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.

Combining Hyperspectral Reflectance and Multivariate Regression Models to Estimate Plant Biomass of Advanced Spring Wheat Lines in Diverse Phenological Stages under Salinity Conditions

“…These findings also suggest that the hyperspectral reflectance tool can be applicable in salinity studies for assessing plant biomass and for effectively screening wheat genotypes at the early growth stage, obviating the need to grow genotypes to maturity, but this suggestion is only applicable by looking at specific wavelengths in the red-edge and SWIR regions as shown in Figure 4. Generally, the spectral reflectance at wavelengths in the VIS, NIR, and SWIR regions is affected primarily by the vegetation growth status (contents of chlorophyll and other photosynthetic pigments), plant biomass status (cumulative plant biomass, canopy structure, and leaf cellular structure), and plant water status (content of canopy water and material absorbed light such as lignin, cellulose, protein, and cell walls), respectively [73][74][75][76][77][78]. According to Munns and Tester [24], plant biomass responds to salinity stress in two phases, referred to as the osmotic phase and ionic phase.…”

Arbuscular mycorrhizal fungi increase crop yields by improving biomass under rainfed condition: a meta-analysis

“…Crop yield is dependent upon photosynthesis and the exchange of carbon metabolites from source to sink tissues ( Oiestad, Martin & Giroux, 2019 ). As an important indicator of the growth and photosynthesis of plant ( Sun et al, 2021 ), the leaf chlorophyll effect size were calculated. Additionally, the resistance of plant is an important indicator to plant growth under rainfed condition.…”

Optimization of Ultrasonic-Assisted Extraction of Active Components and Antioxidant Activity from Polygala tenuifolia: A Comparative Study of the Response Surface Methodology and Least Squares Support Vector Machine

“…Compared with the RSM, least squares support vector machine (LSSVM) is derived from the support vector machine (SVM) approach and is a further improvement of the support vector machine model [ 22 ], which improves the operation speed by transforming the objective function, optimizing the equation conditions, and reducing computational complexity. It has been widely used in many fields [ 23 , 24 ]. A comparative study of RSM and LSSVM can provide stronger evidence while comparing methods in analytical chemistry.…”