AIMC Topic: Triticum

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Detection of sunn pest-damaged wheat grains using artificial bee colony optimization-based artificial intelligence techniques.

Journal of the science of food and agriculture Nov 21, 2019
BACKGROUND: In this study, artificial intelligence models that identify sunn pest-damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG an...
Neural Networks, Computer Artificial Intelligence Seeds Triticum Turkey Algorithms Image Processing, Computer-Assisted Machine Learning
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Quantitative analysis of wheat maltose by combined terahertz spectroscopy and imaging based on Boosting ensemble learning.

Food chemistry Sep 30, 2019
To improve the prediction accuracy of existing data modeling that is based on either spectral data or image data alone, we herein propose a method for the quantitative analysis of wheat maltose contents based on the fusion of terahertz spectroscopy a...
Terahertz Spectroscopy Triticum Maltose Support Vector Machine
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Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Plant physiology Jul 22, 2019
Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m...
Deep Learning Agriculture Triticum Phenotype Wind Breeding Environment Motion Crops, Agricultural Algorithms
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PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution.

BMC genomics Jul 12, 2019
BACKGROUND: Our understanding of polyploid genomes is limited by our inability to definitively assign sequences to a specific subgenome without extensive prior knowledge like high resolution genetic maps or genome sequences of diploid progenitors. In...
Phylogeny Genome, Plant Unsupervised Machine Learning Genomics Repetitive Sequences, Nucleic Acid DNA, Plant Polyploidy Evolution, Molecular Triticum Nicotiana
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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 ...
Temperature Cluster Analysis Iran Triticum Artificial Intelligence
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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...
Aphids Pest Control Plant Diseases Motor Vehicles Animals Robotics Triticum Agriculture
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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...
Triticum Genome, Plant Gene-Environment Interaction Models, Genetic Machine Learning 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...
Gene-Environment Interaction Zea mays Machine Learning Triticum Models, Genetic Sequence Analysis, DNA Quantitative Trait, Heritable Predictive Value of Tests
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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 Nanoparticles Europium Immunoassay Antibodies Limit of Detection Triticum Ochratoxins
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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...
Zea mays Microbial Consortia Biomass Bioreactors Lignin RNA, Ribosomal, 16S Cellulose Polysaccharides Sewage Anaerobiosis Triticum Biodegradation, Environmental Biofuels
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