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

Abstract

Background 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.

Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds

“…In this study, HSI data outperformed machine vision image data in SVM-based models for distinguishing AMM, AM and SM seeds, which aligns well with studies comparing these approaches for classifying kernels of rice ( Fabiyi et al., 2020 ) and maize ( Tu et al., 2022 ), as well as comparing chlorophyll content in sorghum leaves ( Zhang et al., 2022a ). It is likely that machine vision data resulted in lower accuracy in classifying AMM and AM seeds because the features extracted by machine vision were purely morphological phenotypes.…”

Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging

“…Hyperspectral imaging (HSI) has been a powerful tool for the comprehensive analysis of agricultural and food production that integrates spectroscopy and machine vision to collect both spectral and spatial information from one target [ 15 , 16 , 17 , 18 ]. HSI represents the spectra at each pixel of an image, utilizing the advantages of near-infrared spectroscopy, conventional imaging, and even multispectral imaging techniques.…”

OsbHLHq11, the Basic Helix-Loop-Helix Transcription Factor, Involved in Regulation of Chlorophyll Content in Rice

“…Methods for measuring chlorophyll content include a method based on absorption of light by a water-soluble acetone extract of chlorophyll, and a method based on the absorbance or reflectance of light of a specific wavelength by an intact leaf using a portable chlorophyll meter [ 43 ]. However, these destructive methods based on laboratory procedures such as acetone-ethanol extraction, spectrophotometry, and high-performance liquid chromatography are time-consuming, expensive, and not suitable for high-throughput analysis [ 44 ]. Chlorophyll mainly determines the color of plant leaves, which indicates the nutritional and health status of plants, and it has been demonstrated that plant nutrient levels, water availability, plant diseases, and aging have a significant effect on plant leaf color [ 45 ].…”