AIMC Topic: Plant Breeding

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LightGBM: accelerated genomically designed crop breeding through ensemble learning.

Genome biology Sep 20, 2021
LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performa...
Crops, Agricultural Genome-Wide Association Study Plant Breeding Genomics Decision Trees Machine Learning Polymorphism, Single Nucleotide Zea mays Software
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Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program.

The plant genome Sep 5, 2021
Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine- and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Beca...
Artificial Intelligence Triticum Genome, Plant Plant Breeding Deep Learning Genomics Bayes Theorem
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Crop yield prediction integrating genotype and weather variables using deep learning.

PloS one Jun 17, 2021
Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop producti...
Glycine max Plant Breeding Weather Crops, Agricultural Neural Networks, Computer Deep Learning Genotype
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A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits.

Plant communications Jan 29, 2021
Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_...
Phenotype Disease Resistance X-Ray Microtomography Plant Breeding Deep Learning Oryza Plant Diseases
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Automated Counting Grains on the Rice Panicle Based on Deep Learning Method.

Sensors (Basel, Switzerland) Jan 4, 2021
Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. I...
Plant Breeding Edible Grain Phenotype Oryza Automation Deep Learning
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Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data.

Journal of plant physiology Dec 29, 2020
Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the n...
Genome, Plant Plant Breeding Genomics Machine Learning Genotype Phenotype Crops, Agricultural Big Data
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Morphological traits of drought tolerant horse gram germplasm: classification through machine learning.

Journal of the science of food and agriculture Jul 3, 2020
BACKGROUND: Horse gram (Macrotyloma uniflorum (Lam.) Verdc.) is an underutilized pulse crop with good drought resistance traits. It is a rich source of protein. Conventional breeding methods for high yielding and abiotic stress tolerant germplasm are...
Fabaceae Stress, Physiological Quantitative Trait Loci Droughts Plant Breeding Phenotype Machine Learning Genotype Algorithms Seeds
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New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.

G3 (Bethesda, Md.) May 7, 2019
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes...
Selection, Genetic Genome Genome, Plant Quantitative Trait, Heritable Plant Breeding Genotype Genetic Association Studies Models, Genetic Deep Learning Algorithms Phenotype Reproducibility of Results Genomics
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Identification of optimal prediction models using multi-omic data for selecting hybrid rice.

Heredity Mar 25, 2019
Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available fo...
Crops, Agricultural Crosses, Genetic Quantitative Trait Loci Regression Analysis Oryza Quantitative Trait, Heritable Plant Breeding Chimera Support Vector Machine Seeds Metabolomics Genomics Models, Genetic
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A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding.

G3 (Bethesda, Md.) Feb 7, 2019
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic ba...
Quantitative Trait, Heritable Support Vector Machine Plant Breeding Bayes Theorem Selective Breeding
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