AIMC Topic: Triticum

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Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.

The plant genome Mar 1, 2025
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide pol...
Machine Learning Genomics Remote Sensing Technology Triticum Hyperspectral Imaging Polymorphism, Single Nucleotide Heat-Shock Response
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Assessing the performance of generative artificial intelligence in retrieving information against manually curated genetic and genomic data.

Database : the journal of biological databases and curation Feb 17, 2025
Curated resources at centralized repositories provide high-value service to users by enhancing data veracity. Curation, however, comes with a cost, as it requires dedicated time and effort from personnel with deep domain knowledge. In this paper, we ...
Quantitative Trait Loci Generative Artificial Intelligence Databases, Genetic Data Curation Data Mining Artificial Intelligence Triticum Genome, Plant Hordeum Genomics Humans
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MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.

Journal of experimental botany Jan 10, 2025
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we i...
Algorithms Magnetic Resonance Imaging Seeds Triticum Phenotype Deep Learning Hordeum
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LOGOWheat: deep learning-based prediction of regulatory effects for noncoding variants in wheats.

Briefings in bioinformatics Nov 22, 2024
Identifying the regulatory effects of noncoding variants presents a significant challenge. Recently, the accumulation of epigenomic profiling data in wheat has provided an opportunity to model the functional impacts of these variants. In this study, ...
Genome, Plant Triticum Genetic Variation Software Deep Learning Computational Biology Epigenomics
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Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.

G3 (Bethesda, Md.) Nov 6, 2024
In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, g...
Triticum Quantitative Trait Loci Polymorphism, Single Nucleotide Models, Genetic Algorithms Phenotype Genome-Wide Association Study Genomics Animals Neural Networks, Computer
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[Grain yield estimation of wheat-maize rotation cultivated land based on Sentinel-2 multi-spectral image: A case study in Caoxian County, Shandong, China].

Ying yong sheng tai xue bao = The journal of applied ecology Dec 1, 2023
Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong...
Edible Grain Zea mays Triticum China Remote Sensing Technology
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Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

Bioinformatics (Oxford, England) Jun 1, 2023
MOTIVATION: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plan...
Deep Learning Genomics Phenotype Phenomics Edible Grain Selection, Genetic Genotype Triticum Plant Breeding
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Multimodal deep learning methods enhance genomic prediction of wheat breeding.

G3 (Bethesda, Md.) May 2, 2023
While several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant breeding research, few methods have linked genomics and phenomics (imaging). Deep ...
Triticum Plant Breeding Phenotype Genotype Deep Learning Models, Genetic Genomics
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Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

Journal of experimental botany Nov 2, 2022
A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop g...
Deep Learning Chlorophyll Plant Leaves Plant Breeding Water Triticum
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High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat.

GigaScience Nov 1, 2019
BACKGROUND: Measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genome...
Gene Expression Regulation, Plant Flowers Deep Learning Genetic Association Studies Genotype Databases, Nucleic Acid Triticum
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