Abstract
BackgroundLeaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput phenotyping. High throughput imaging techniques are now widely used for nondestructive analysis of plant phenotypic traits. In this study three imaging modules, namely, RGB, hyperspectral, and fluorescence imaging, were used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and regression models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. ResultsModels that included two additional variables, DAS (day after sowing) and SLW (specific leaf weight), were also investigated to improve the prediction of chlorophyll. R2 for chlorophyll concentration for multiple linear models at various color components were 0.77 for R, 0.79 for G, 0.70 for B. To obtain additional spectral information, color component H, S, and I were calculated after color spaces being transformed. The result of HSI space showed that R2 for chlorophyll concentration for multiple linear models were 0.67 for H, 0.88 for S, 0.77 for I. The R2 values for different hyperspectral index like the ratio vegetation index (RVI), the normalized difference vegetation index (NDVI), modified chlorophyll absorption ratio index (MCARI) between 0.77 and 0.78. R2=0.79 was obtained with fluorescence image. Partial least squares regression (PLSR) was employed to using the selected vegetation indices computed from different imaging data to estimate the chlorophyll concentration for sorghum plants. Among all the imaging data, chlorophyll content was predicted with high accuracy (R2 from 0.84 to 2.92, RPD from 2.49 to 3.58). ConclusionAccording to the Akaike's Information Criterion (AIC) error function, the model was better fitted based on images, DAS and SLW than that based on images and DAS. This study indicated that the accuracy for chlorophyll estimation was increased by the image traits combined with DAS and SLW. High throughput imaging provides a simple, rapid, and nondestructive method to estimate the leaf chlorophyll concentration.
Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins