AIMC Topic: Triticum

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Climate data clustering effects on arid and semi-arid rainfed wheat yield: a comparison of artificial intelligence and K-means approaches.

International journal of biometeorology May 22, 2019
Clustering algorithms are critical data mining techniques used to analyze a wide range of data. This study compares the utility of ant colony optimization (ACO), genetic algorithm (GA), and K-means methods to cluster climatic variables affecting the ...
Cluster Analysis Triticum Iran Artificial Intelligence Temperature
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Field evaluation of an unmanned aerial vehicle (UAV) sprayer: effect of spray volume on deposition and the control of pests and disease in wheat.

Pest management science Feb 14, 2019
BACKGROUND: Unmanned aerial vehicles (UAVs) are a recently developed aerial spraying technology. However, the effect of spray volume variation on deposition and pesticide control efficacy is unknown. The effect of three UAV spray volumes (9.0, 16.8 a...
Plant Diseases Animals Motor Vehicles Agriculture Aphids Robotics Pest Control Triticum
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Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.

G3 (Bethesda, Md.) Dec 10, 2018
Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of ge...
Machine Learning Models, Genetic Triticum Genome, Plant Gene-Environment Interaction Sequence Analysis, DNA Zea mays
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Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture.

G3 (Bethesda, Md.) Dec 10, 2018
Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of gen...
Triticum Sequence Analysis, DNA Quantitative Trait, Heritable Predictive Value of Tests Machine Learning Gene-Environment Interaction Zea mays Models, Genetic
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Development of a new format of competitive immunochromatographic assay using secondary antibody-europium nanoparticle conjugates for ultrasensitive and quantitative determination of ochratoxin A.

Food chemistry Sep 19, 2018
In this study, to enhance the sensitivity of conventional immunochromatographic assay (ICA) two strategies including using a high sensitive label and changing the assay format, were simultaneously applied to develop an ultrasensitive format of ICA. I...
Antibodies, Immobilized Europium Immunoassay Nanoparticles Ochratoxins Antibodies Limit of Detection Triticum
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Enrichment of thermophilic and mesophilic microbial consortia for efficient degradation of corn stalk.

Journal of environmental sciences (China) Jul 29, 2018
Six different environmental samples were applied to enrich microbial consortia for efficient degradation of corn stalk, under the thermophilic and mesophilic conditions. The consortium obtained from anaerobic digested sludge under thermophilic condit...
Microbial Consortia Zea mays Bioreactors Triticum Cellulose Anaerobiosis Biomass Lignin Polysaccharides RNA, Ribosomal, 16S Sewage Biodegradation, Environmental Biofuels
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An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jan 12, 2018
Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or ...
Introduced Species Calibration Gasoline Algorithms Plant Weeds Triticum Wine Least-Squares Analysis Artificial Intelligence
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Grain classifier with computer vision using adaptive neuro-fuzzy inference system.

Journal of the science of food and agriculture Mar 6, 2017
BACKGROUND: A computer vision-based classifier using an adaptive neuro-fuzzy inference system (ANFIS) is designed for classifying wheat grains into bread or durum. To train and test the classifier, images of 200 wheat grains (100 for bread and 100 fo...
Triticum Photography Neural Networks, Computer Fuzzy Logic
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Modeling of glucose release from native and modified wheat starch gels during in vitro gastrointestinal digestion using artificial intelligence methods.

International journal of biological macromolecules Jan 20, 2017
Estimation of the amounts of glucose release (AGR) during gastrointestinal digestion can be useful to identify food of potential use in the diet of individuals with diabetes. In this work, adaptive neuro-fuzzy inference system (ANFIS), genetic algori...
Models, Biological Starch Gastrointestinal Tract Neural Networks, Computer Digestion Gels Triticum Glucose
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Protease inhibitors in various flours and breads: Effect of fermentation, baking and in vitro digestion on trypsin and chymotrypsin inhibitory activities.

Food chemistry Dec 18, 2016
In this study trypsin (TIA) and chymotrypsin inhibitory (CIA) activities were determined in the extracts of wheat, rye mix, mixed cereals and, whole wheat flours and, breads made with these flours. In addition, effects of fermentation, baking and in ...
Cooking Bread Edible Grain Flour Triticum Chymotrypsin Trypsin Fermentation Protease Inhibitors Secale Digestion
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