scholarly article | Q13442814 |
P819 | ADS bibcode | 2019NatSR...917132P |
P356 | DOI | 10.1038/S41598-019-53451-4 |
P932 | PMC publication ID | 6868245 |
P698 | PubMed publication ID | 31748577 |
P50 | author | Asheesh K Singh | Q88323389 |
P2093 | author name string | Soumik Sarkar | |
Baskar Ganapathysubramanian | |||
Kyle A Parmley | |||
Race H Higgins | |||
P2860 | cites work | Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding | Q90021619 |
Multi-objective optimized genomic breeding strategies for sustainable food improvement | Q91826564 | ||
Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean | Q104464351 | ||
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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 Plants | Q26849782 | ||
Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data | Q31152770 | ||
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data | Q33480662 | ||
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 Wheat | Q37242027 | ||
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 Yield | Q38671838 | ||
Computer vision and machine learning for robust phenotyping in genome-wide studies | Q38921290 | ||
Historical gains in soybean (Glycine max Merr.) seed yield are driven by linear increases in light interception, energy conversion, and partitioning efficiencies | Q39199715 | ||
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.) Merrill1 | Q56094582 | ||
Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives | Q57146473 | ||
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 products | Q57314630 | ||
Greedy function approximation: A gradient boosting machine | Q57532752 | ||
Genetic Progress in Grain Yield and Physiological Traits in Chinese Wheat Cultivars of Southern Yellow and Huai Valley since 1950 | Q58291635 | ||
Red edge spectral measurements from sugar maple leaves | Q58401029 | ||
Genetic Improvement Rates of Short-Season Soybean Increase with Plant Population | Q61940358 | ||
Building Predictive Models inRUsing thecaretPackage | Q75168729 | ||
Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding | Q87965215 | ||
P433 | issue | 1 | |
P921 | main subject | machine learning | Q2539 |
P304 | page(s) | 17132 | |
P577 | publication date | 2019-11-20 | |
P1433 | published in | Scientific Reports | Q2261792 |
P1476 | title | Machine Learning Approach for Prescriptive Plant Breeding | |
P478 | volume | 9 |
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