Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components.

Photosynthesis research - 1 Jun 2014

Riccardi M, Mele G, Pulvento C, Lavini A, d'Andria R, Jacobsen S-E

Abstract excerpt

Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Chlorophyll

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Genotype

Share to Photography

Share to Plant Leaves

Share to Regression Analysis

Share to Spectrum Analysis

Source

Identifiers

PMID: 24442792
DOI: 10.1007/s11120-014-9970-2

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components.PMID 24442792Read ↗

Assessing Contents of Sugars, Vitamins, and Nutrients in Baby Leaf Lettuce from Hyperspectral Data with Machine Learning ModelsSemantic proximity Read ↗

Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction.Semantic proximity Read ↗

Multi-scale predictive modeling of phenology and carotenoid content in carrots using spectral techniques, colorimetry, and artificial intelligence.Semantic proximity Read ↗

Spectral analysis for the early detection of anthracnose in fruits of Sugar Mango (Mangifera indica)Semantic proximity Read ↗

The application of fluorescence spectroscopy and machine learning as non-destructive approach to distinguish two different varieties of greenhouse tomatoesSemantic proximity Read ↗

Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes.Semantic proximity Read ↗

Multivariate analysis for agronomic, physiological, macro, and micronutrient traits of exotic vegetable amaranth genotypes.Semantic proximity Read ↗

Determination of optimum harvest maturity and non-destructive evaluation of pod development and maturity in cacao (Theobroma cacao L.) using a multiparametric fluorescence sensor.Semantic proximity Read ↗

Fast detection of leaf pigments and isoprenoids for ecophysiological studies, plant phenotyping and validating remote-sensing of vegetation.Semantic proximity Read ↗

On-field monitoring of fruit ripening evolution and quality parameters in olive mutants using a portable NIR-AOTF device.Semantic proximity Read ↗

Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information.Semantic proximity Read ↗

Non destructive estimation of total phenol and crude fiber content in intact seeds of rapeseed–mustard using FTNIRSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.