AIMC Topic: Plant Breeding

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Sweet pepper yield modeling via deep learning and selection of superior genotypes using GBLUP and MGIDI.

Scientific reports
Intelligent knowledge about Capsicum annuum L. germplasm could lead to effective management of germplasm. Here, 29 accessions of sweet pepper were investigated in two separate randomized complete block design with three replications in the field cond...

Breaking down data silos across companies to train genome-wide predictions: A feasibility study in wheat.

Plant biotechnology journal
Big data, combined with artificial intelligence (AI) techniques, holds the potential to significantly enhance the accuracy of genome-wide predictions. Motivated by the success reported for wheat hybrids, we extended the scope to inbred lines by integ...

EBMGP: a deep learning model for genomic prediction based on Elastic Net feature selection and bidirectional encoder representations from transformer's embedding and multi-head attention pooling.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Enhancing early selection through genomic estimated breeding values is pivotal for reducing generation intervals and accelerating breeding programs. Recently, deep learning (DL) approaches have gained prominence in genomic prediction (GP). Here, we i...

Biological Prior Knowledge-Embedded Deep Neural Network for Plant Genomic Prediction.

Genes
Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed mode...

Application of machine learning and genomics for orphan crop improvement.

Nature communications
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
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
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
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
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
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...