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

• Published in: Plant methods 2022-05, Vol.18 (1), p.60-60, Article 60
• Main Authors: Zhang, Huichun, Ge, Yufeng, Xie, Xinyan, Atefi, Abbas, Wijewardane, Nuwan K, Thapa, Suresh
• Format: Article
• Language: English
• Subjects: Analysis; Chlorophyll; Chlorophyll content; Color; Color imagery; Digital cameras; Ethanol; Feature extraction; Fluorescence; Growth; High performance liquid chromatography; High throughput; High-throughput screening (Biochemical assaying); Hyperspectral imaging; Image analysis; Image processing; Imaging techniques; Infrared spectroscopy; Least squares method; Leaves; Liquid chromatography; Methods; Modules; Nondestructive testing; Normalized difference vegetative index; Nutrient content; Nutrient status; Partial least squares regression; Performance prediction; Phenotyping; Physiology; Plant phenotyping; Regression analysis; Regression models; Remote sensing; Sensors; Sorghum; Specific leaf weight; Spectrophotometry; Spectrum analysis; Unmanned aerial vehicles; Vegetation; Vegetation index
• ISSN: 1746-4811; 1746-4811
• DOI: 10.1186/s13007-022-00892-0

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. The models with a single color feature from RGB images predicted chlorophyll content with R 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 R ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R 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 R 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. 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.