AIMC Topic: Plant Breeding
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Application of machine learning and genomics for orphan crop improvement.
Nature communications
Jan 24, 2025
Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources avail...
Cropformer: An interpretable deep learning framework for crop genomic prediction.
Plant communications
Dec 16, 2024
Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models ...
Synthetic biology and artificial intelligence in crop improvement.
Plant communications
Dec 12, 2024
Synthetic biology plays a pivotal role in improving crop traits and increasing bioproduction through the use of engineering principles that purposefully modify plants through "design, build, test, and learn" cycles, ultimately resulting in improved b...
Machine learning-enhanced multi-trait genomic prediction for optimizing cannabinoid profiles in cannabis.
The Plant journal : for cell and molecular biology
Nov 27, 2024
Cannabis sativa L., known for its medicinal and psychoactive properties, has recently experienced rapid market expansion but remains understudied in terms of its fundamental biology due to historical prohibitions. This pioneering study implements GS ...
Big data and artificial intelligence-aided crop breeding: Progress and prospects.
Journal of integrative plant biology
Oct 28, 2024
The past decade has witnessed rapid developments in gene discovery, biological big data (BBD), artificial intelligence (AI)-aided technologies, and molecular breeding. These advancements are expected to accelerate crop breeding under the pressure of ...
Machine learning algorithms translate big data into predictive breeding accuracy.
Trends in plant science
Oct 26, 2024
Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability...
Artificial intelligence in plant breeding.
Trends in genetics : TIG
Aug 7, 2024
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scien...
Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Jul 23, 2024
Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with hi...
Machine learning for genomic and pedigree prediction in sugarcane.
The plant genome
Jun 26, 2024
Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome predicti...
Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.
The plant genome
Jun 9, 2024
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivaleno...
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