AIMC Topic: Plant Breeding

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Precision breeding in a changing climate: unlocking resilience through omics and gene editing.

Functional & integrative genomics
Climate change, rising global food demand, and shrinking resources require transformative innovations in crop breeding. This review outlines recent advances in new breeding technologies (NBTs), including molecular markers, genome-wide association stu...

Challenges and Opportunities with CRISPR-Based Genome Editing in Legume Crops.

Functional & integrative genomics
Over the last couple of decades, tremendous progress has been made in legume genomics. Genomics information generated for legume crops is being explored through molecular breeding and transgenic approaches. However, the gap between knowledge generati...

Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
This study presents a Bayesian neural networks framework with LASSO regularization and the GSMeSP interpretability tool, enabling accurate, uncertainty-aware, and biologically interpretable genomic prediction. Deep learning offers significant potenti...

Phenotype-driven leaf deep metabolomics framework depicts key metabolisms and metabolites associated with yield traits in rice.

Planta
This study links rice leaf metabolome to yield traits, identifying 13 key metabolites through computational metabolomics. These enable early prediction of high-yield varieties, enhancing screening strategies in crop breeding. Metabolites serve as dyn...

Harnessing multi-omics and genome-editing technologies for climate-resilient agriculture: bridging AI-driven insights with sustainable crop improvement.

Plant molecular biology
Environmental challenges such as drought, salinity, heavy metal contamination, and nutrient deficiencies threaten global agricultural productivity and food security. These stressors drastically reduce crop yields, necessitating innovative solutions. ...

A comparative study highlights superiority of LSTM in crop genomic prediction.

Planta
We systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods, and found LSTM suitable for capturing additive and epistatic effects. Genomic pre...

Next-generation molecular breeding tools to harness higher genetic gains in sugarcane.

Planta
Next-generation molecular tools with AI integration can accelerate genetic gain in sugarcane by enhancing variation, accuracy, and efficiency, enabling rapid development of high-yielding, high-quality, and climate-resilient varieties. Enhancing genet...

Integrative genomics and genetics from evolutionary insights to precision breeding in peanuts (Arachis Hypogaea L.).

Functional & integrative genomics
Peanut (Arachis hypogaea L.), a globally important oilseed crop, increasingly challenged by rising edible oil demands as well as biotic and abiotic stresses. This review synthesizes recent advances in peanut genomics, evolutionary biology, and breedi...

Towards smart agriculture: AI-driven prediction of key genes for revolutionizing crop breeding.

Planta
AI-driven key gene prediction is revolutionizing crop breeding, enhancing precision, efficiency, and sustainability while paving the way for intelligent, data-driven agricultural innovation. The integration of artificial intelligence (AI) into crop b...

Identifying graft incompatible rootstocks for sweet cherry through machine learning algorithms.

PloS one
Graft incompatibility is a key factor in the development of dwarf and semi dwarf rootstocks for sweet cherry (Prunus avium L.) to improve yield, fruit quality, precocity, and labor efficiency. This study evaluated the graft incompatibility of eight g...