AIMC Topic: Hordeum

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Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches.

Scientific reports Jun 4, 2025
By employing machine-learning models, this study utilizes agronomical and molecular features to predict powdery mildew disease resistance in Barley (Hordeum Vulgare L). A 130-line F8-F9 barley population caused Badia and Kavir to grow at the Gonbad K...
Machine Learning Algorithms Phenotype Neural Networks, Computer Hordeum Ascomycota Disease Resistance Bayes Theorem Plant Diseases
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Predicting land suitability for wheat and barley crops using machine learning techniques.

Scientific reports May 7, 2025
Ensuring food security to meet the demands of a growing population remains a key challenge, especially for developing countries like Ethiopia. There are various policies and strategies designed by the government and stakeholders to confront the chall...
Hordeum Machine Learning Agriculture Ethiopia Triticum Crops, Agricultural
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Identification of Fusarium sambucinum species complex by surface-enhanced Raman spectroscopy and XGBoost algorithm.

Food chemistry Mar 12, 2025
Rapid and reliable identification of Fusarium fungi is crucial, due to their role in food spoilage and potential toxicity. Traditional identification methods are often time-consuming and resource-intensive. This study explores the use of surface-enha...
Hordeum Spectrum Analysis, Raman Boosting Machine Learning Algorithms Fusarium
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Machine learning discovery of novel antihypertensive peptides from highland barley protein inhibiting angiotensin I-converting enzyme (ACE).

Food research international (Ottawa, Ont.) Jan 6, 2025
Hypertension is a major global health concern, and there is a need for new antihypertensive agents derived from natural sources. This study aims to identify novel angiotensin I-converting enzyme (ACE) inhibitors from bioactive peptides derived from f...
Molecular Docking Simulation Antihypertensive Agents Machine Learning Peptides Angiotensin-Converting Enzyme Inhibitors Hordeum Peptidyl-Dipeptidase A Molecular Dynamics Simulation Plant Proteins Humans
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Machine learning-enhanced modeling and characterization for optimizing dietary Fiber production from Highland barley bran.

International journal of biological macromolecules Nov 14, 2024
This study investigated the modification of highland barley bran through co-fermentation of Lactobacillus bulgaricus and Kluyveromyces marxianus, and developed a dynamic prediction model for DF content under these co-fermentation conditions using mac...
Hordeum Dietary Fiber Machine Learning Kluyveromyces Fermentation Lactobacillus delbrueckii
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Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Journal of agricultural and food chemistry Nov 13, 2024
Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples (genotype-location-year combinations) from Cali...
Edible Grain Plant Proteins Environment Hordeum Mass Spectrometry Seeds Proteomics Proteome Machine Learning Genotype
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Deep Learning-Based Barley Disease Quantification for Sustainable Crop Production.

Phytopathology Sep 13, 2024
Net blotch disease caused by is a major fungal disease that affects barley () plants and can result in significant crop losses. In this study, we developed a deep learning model to quantify net blotch disease symptoms on different days postinfection...
Plant Leaves Crops, Agricultural Hordeum Ascomycota Plant Diseases Neural Networks, Computer Deep Learning Crop Production
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Yield prediction with machine learning algorithms and satellite images.

Journal of the science of food and agriculture Aug 24, 2020
BACKGROUND: Barley is one of the strategic agricultural products available in the world, and yield prediction is important for ensuring food security. One way of estimating a product is to use remote sensing data in conjunction with field data and me...
Seasons Hordeum Algorithms Agriculture Remote Sensing Technology Machine Learning
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Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik May 24, 2020
We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, buil...
Genome, Plant Linkage Disequilibrium Machine Learning Chromosome Mapping Genotype Algorithms Hordeum
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Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.

Communications biology Apr 15, 2020
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segme...
Seeds Crops, Agricultural Deep Learning Neural Networks, Computer Hordeum Image Processing, Computer-Assisted Phenotype
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