AIMC Topic: Triticum

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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 China Remote Sensing Technology Triticum
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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 Genotype Phenotype Selection, Genetic Plant Breeding Phenomics Triticum Edible Grain
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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 ...
Plant Breeding Deep Learning Phenotype Genomics Models, Genetic Genotype Triticum
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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 Water Chlorophyll Plant Leaves Plant Breeding 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...
Genotype Genetic Association Studies Triticum Deep Learning Gene Expression Regulation, Plant Flowers Databases, Nucleic Acid
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Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.

The plant genome Jul 1, 2018
New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive natur...
Plant Breeding Plant Diseases Linear Models Disease Resistance Genome, Plant Basidiomycota Support Vector Machine Triticum Genomics Models, Genetic Neural Networks, Computer Machine Learning
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Use of in vitro dry matter digestibility and gas production to predict apparent total tract digestibility of total dietary fiber for growing pigs.

Journal of animal science Dec 1, 2017
In vitro DM disappearance (IVDMD) and gas production methods have been developed and used to measure in vivo nutrient digestibility of feed ingredients, but further validation is needed for ingredients containing high concentrations of insoluble fibe...
Animal Feed Fermentation Gastrointestinal Tract Digestion Triticum Swine Glycine max Dietary Fiber Animals Diet Zea mays
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Cinnamic acid and fish flour affect wheat phenolic acids and flavonoid compounds, lipid peroxidation, proline levels under salt stress.

Acta biologica Hungarica Dec 1, 2017
To elucidate the physiological mechanism of salt stress mitigated by cinnamic acid (CA) and fish flour (FF) pretreatment, wheat was pretreated with 20, 50 and 100 ppm CA and 1 g/10 mL FF for 2 d and was then cultivated. We investigated whether exogen...
Fish Flour Flavonoids Osmotic Pressure Triticum Cinnamates Lipid Peroxidation Hydroxybenzoates
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Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

GigaScience Oct 1, 2017
Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a...
Algorithms Image Processing, Computer-Assisted Plant Roots Triticum Seedlings Machine Learning Quantitative Trait Loci
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Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

GigaScience Oct 1, 2017
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation...
Plants Plant Shoots Machine Learning Phenotype Quantitative Trait Loci Plant Roots Triticum
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