Article

Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals

2022-02-25

Abstract excerpt

Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, suc...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 415071b5-1973-59e1-a45c-49c63369894a
DOI: 10.20944/preprints202202.0335.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain CerealsDOI 10.20944/preprints202202.0335.v1Read

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging.Semantic proximity Read

ProTformer: Transformer-based model for superior prediction of protein content in lablab bean (Lablab purpureus L.) using Near-Infrared Reflectance spectroscopy.Semantic proximity Read

Integration of High-Throughput Water-Sensitive Phenotyping for Crop Water Demand Diagnosis: Technical Pathways, Research Progress, and ChallengesSemantic proximity Read

Prediction of CP and starch concentrations in ruminal in situ studies and ruminal degradation of cereal grains using NIRS.Semantic proximity Read

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.Semantic proximity Read

Structure-property machine learning models with predictive capabilities for glycans in foodSemantic proximity Read

Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy.Semantic proximity Read

Precision Agriculture using Machine Learning and Deep Learning Algorithms: A Comprehensive StudySemantic proximity Read

Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.Semantic proximity Read

High-throughput phenotyping using VIS/NIR spectroscopy in the classification of soybean genotypes for grain yield and industrial traits.Semantic proximity Read

Machine learning for major food crops breeding: Applications, challenges, and ways forwardSemantic proximity Read

Simultaneously prediction of multiple wheat leaf phenotypes using hyperspectral imaging with multi-task machine and deep learning.Semantic proximity Read