Machine Learning Approach for Prescriptive Plant Breeding

scientific article published on 20 November 2019

Machine Learning Approach for Prescriptive Plant Breeding is …
instance of (P31):
scholarly articleQ13442814

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P819ADS bibcode2019NatSR...917132P
P356DOI10.1038/S41598-019-53451-4
P932PMC publication ID6868245
P698PubMed publication ID31748577

P50authorAsheesh K SinghQ88323389
P2093author name stringSoumik Sarkar
Baskar Ganapathysubramanian
Kyle A Parmley
Race H Higgins
P2860cites workModelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breedingQ90021619
Multi-objective optimized genomic breeding strategies for sustainable food improvementQ91826564
Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in SoybeanQ104464351
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Gene selection and classification of microarray data using random forest.Q25255911
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Machine Learning for High-Throughput Stress Phenotyping in PlantsQ26849782
Predicting grain yield using canopy hyperspectral reflectance in wheat breeding dataQ31152770
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral dataQ33480662
Genetic Architecture of Phenomic-Enabled Canopy Coverage in Glycine max.Q33877438
MissForest--non-parametric missing value imputation for mixed-type data.Q34062067
What variables are important in predicting bovine viral diarrhea virus? A random forest approach.Q35886184
Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in WheatQ37242027
Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?Q37689473
Field high-throughput phenotyping: the new crop breeding frontier.Q38153969
Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain YieldQ38671838
Computer vision and machine learning for robust phenotyping in genome-wide studiesQ38921290
Historical gains in soybean (Glycine max Merr.) seed yield are driven by linear increases in light interception, energy conversion, and partitioning efficienciesQ39199715
Genetic Characterization of the Soybean Nested Association Mapping Population.Q47693059
Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.Q48250933
An explainable deep machine vision framework for plant stress phenotyping.Q55412932
Stage of Development Descriptions for Soybeans, Glycine Max (L.) Merrill1Q56094582
Deep Learning for Plant Stress Phenotyping: Trends and Future PerspectivesQ57146473
Imputation of missing data in life-history trait datasets: which approach performs the best?Q57193797
Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial productsQ57314630
Greedy function approximation: A gradient boosting machineQ57532752
Genetic Progress in Grain Yield and Physiological Traits in Chinese Wheat Cultivars of Southern Yellow and Huai Valley since 1950Q58291635
Red edge spectral measurements from sugar maple leavesQ58401029
Genetic Improvement Rates of Short-Season Soybean Increase with Plant PopulationQ61940358
Building Predictive Models inRUsing thecaretPackageQ75168729
Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat BreedingQ87965215
P433issue1
P921main subjectmachine learningQ2539
P304page(s)17132
P577publication date2019-11-20
P1433published inScientific ReportsQ2261792
P1476titleMachine Learning Approach for Prescriptive Plant Breeding
P478volume9

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cites work (P2860)
Q93014295Computer vision and machine learning enabled soybean root phenotyping pipeline
Q97530705Performance prediction of crosses in plant breeding through genotype by environment interactions

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