AIMC Topic: Plant Breeding

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Artificial neural networks as auxiliary tools for the improvement of bean plant architecture.

Genetics and molecular research : GMR
Classification using a scale of visual notes is a strategy used to select erect bean plants in order to improve bean plant architectures. Use of morphological traits associated with the phenotypic expression of bean architecture in classification pro...

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability.

Genetics and molecular research : GMR
Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean g...

Genomic and hyperspectral imaging-based prediction blending enables selection for reduced deoxynivalenol content in wheat grains.

G3 (Bethesda, Md.)
Breeding for low deoxynivalenol (DON) mycotoxin content in wheat is challenging due to the complexity of the trait and phenotyping limitations. Since phenomic prediction relies on nonadditive effects and genomic prediction on additive effects, their ...

Mapping QTLs for PHS resistance and development of a deep learning model to measure PHS rate in japonica rice.

The plant genome
Rice (Oryza sativa L.) is a staple food for more than half of the global population. Preharvest sprouting (PHS), which reduces yield and grain quality, presents a major challenge for rice production. The development of PHS-resistant varieties is a ma...

A review of the journey of field crop phenotyping: From trait stamp collections and fancy robots to phenomics-informed crop performance predictions.

Journal of plant physiology
Crop phenotyping encompasses methodologies for measuring plant growth, architecture, and composition with high precision across scales, from organs to canopies. Field-based phenotyping is pivotal in bridging genomic data with crop performance, offeri...

Genomics-assisted breeding for designing salinity-smart future crops.

Plant biotechnology journal
Climate change induces many abiotic stresses, including soil salinity, significantly challenging global agriculture. Salinity stress tolerance (SST) is a complex trait, both physiologically and genetically, and is conferred at various levels of plant...

Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).

The plant genome
For important food crops such as the common bean (Phaseolus vulgaris, L.), global demand continues to outpace the rate of genetic gain for quantitative traits. In this study, we leveraged the multi-environment trial (MET) dataset from the cooperative...

Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates.

The plant genome
Achieving significant genetic gains in grain yield (GY) in wheat (Triticum aestivum L.) requires optimization of the key biomass partitioning traits such as spike partitioning index (SPI) and fruiting efficiency (FE). However, traditional manual phen...

Genomic selection: Essence, applications, and prospects.

The plant genome
Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype-phenotype relationship...

Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food...