ArticlePlant methods2019
Predicting the quality of ryegrass using hyperspectral imaging.
Article in Plant methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Visible-Near-Infrared Hyperspectral Imaging Enables Nondestructive Identification of Bean Accessions via 1D Spectral Reflectance Analysis.ACS omega · 2026Article
- Near-infrared prediction of total phosphorus in leaves content in korla fragrant pear with growth period specificity via spectral modeling.Frontiers in plant science · 2025Article
- A review ofHeliyon · 2024Review
- Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves ofSensors (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
- Article
- Rapid Quantification Method for Yield, Calorimetric Energy and ChlorophyllPlants (Basel, Switzerland) · 2022Article
- Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging.Foods (Basel, Switzerland) · 2022Article
- Editorial: Spectroscopy for crop and product phenotyping.Frontiers in plant science · 2022Article
- Opportunities and limits of controlled-environment plant phenotyping for climate response traits.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Review
- Estimation of amino acid contents in maize leaves based on hyperspectral imaging.Frontiers in plant science · 2022Article
- Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield.Scientific reports · 2021Article
- The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines.Plant phenomics (Washington, D.C.) · 2021Article
- Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm.Plants (Basel, Switzerland) · 2020Review
- Identification of Weeds Based on Hyperspectral Imaging and Machine Learning.Frontiers in plant science · 2020Article
- Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.Frontiers in plant science · 2020Article
- A Non-destructive Method to Quantify Leaf Starch Content in Red Clover.Frontiers in plant science · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe quality of forage plants is a crucial component of animal performance and a limiting factor in pasture based production systems. Key forage attributes that may require improvement include the sugar, lipid, protein and energy contents of the vegetative parts of these plants. The aim of this study was to evaluate the potential capacity of hyperspectral imaging (HSI) for non-invasive assessment of forage chemical composition. Hyperspectral image data within the visible near-infrared range into the extended near-infrared covering 550-1700 nm wavelengths were obtained from 185 accessions of ryegrass (
resultsMedium to high predictive power was observed for the HSI models of total sugars (R
conclusionThis study paves the way for the HSI technology to be used for in-field estimation of forage composition attributes in perennial ryegrass. This will allow more rapid genetic-based selection and breeding for a trait that is normally expensive to measure providing a cheaper, non-destructive and high throughput screening tool.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.