ArticlePlant methods2022
High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion.
Article in Plant methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 114 citations in OpenAlex.
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- Precise Image Color Correction Based on Dual Unmanned Aerial Vehicle Cooperative Flight.Plant phenomics (Washington, D.C.) · 2025Article
- The Scarlet Alchemy of Survival: Integrated Transcriptomic and Metabolomic Analysis of Leaf Coloration in EndangeredPlants (Basel, Switzerland) · 2025Article
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- Effect of biostimulants combined with fertilization on yield and nutritional value of wheat crops.BMC plant biology · 2025Article
- Phenols Extraction from Sorghum Byproducts: Upcycling Strategies and Food Applications.Antioxidants (Basel, Switzerland) · 2025Article
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- A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives.Sensors (Basel, Switzerland) · 2025Review
- Developing a New Method of Transformation for Obtaining XYZ Color Values from RGB Images for Agricultural Applications.Sensors (Basel, Switzerland) · 2024Article
- Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse.Sensors (Basel, Switzerland) · 2024Article
- Aminophenylboronic acid-modified nitrogen-doped graphene quantum dots and their applications in lysine sensing based on interplaying fluorescent mechanisms.Mikrochimica acta · 2024Article
- Nutrients from spawning salmon influence leaf area, tissue density, and nitrogen-15 in riparian plant leaves.Ecology and evolution · 2024Article
- High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images.Plants (Basel, Switzerland) · 2024Article
- Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra.Forestry research · 2024Article
- Plant Physiological Analysis to Overcome Limitations to Plant Phenotyping.Plants (Basel, Switzerland) · 2023Review
- The Impact of 2-Ketones Released from Solid Lipid Nanoparticles on Growth Modulation and Antioxidant System ofPlants (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
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 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.
resultsThe models with a single color feature from RGB images predicted chlorophyll content with R
conclusionAll 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.
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