High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Authors

Huichun Zhang, Nanjing Forestry UniversityFollow
Yufeng Ge, University of Nebraska - LincolnFollow
Xinyan Xie, University of Nebraska - LincolnFollow
Abbas Atefi, University of Nebraska-LincolnFollow
Nuwan Wijewardane,, Mississippi State UniversityFollow
Suresh Thapa, University of Nebraska-LincolnFollow

ORCID IDs

Huichun Zhang http://orcid.org/0000-0002-6479-0641

Date of this Version

2022

Citation

Zhang et al. Plant Methods (2022) 18:60

https://doi.org/10.1186/s13007-022-00892-0

PMCID: PMC9063379

Comments

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License

Abstract

Leaf 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 analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, 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 multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.