[PDF][PDF] Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

Y Xu, X Zhang, H Li, H Zheng, J Zhang, MS Olsen… - Molecular Plant, 2022 - cell.com
The first paradigm of plant breeding involves direct selection-based phenotypic observation,
followed by predictive breeding using statistical models for quantitative traits constructed …

Machine learning bridges omics sciences and plant breeding

J Yan, X Wang - Trends in Plant Science, 2023 - cell.com
Some of the biological knowledge obtained from fundamental research will be implemented
in applied plant breeding. To bridge basic research and breeding practice, machine learning …

Artificial intelligence in food safety: A decade review and bibliometric analysis

Z Liu, S Wang, Y Zhang, Y Feng, J Liu, H Zhu - Foods, 2023 - mdpi.com
Artificial Intelligence (AI) technologies have been powerful solutions used to improve food
yield, quality, and nutrition, increase safety and traceability while decreasing resource …

A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

M John, F Haselbeck, R Dass, C Malisi… - Frontiers in Plant …, 2022 - frontiersin.org
Genomic selection is an integral tool for breeders to accurately select plants directly from
genotype data leading to faster and more resource-efficient breeding programs. Several …

Yield prediction through integration of genetic, environment, and management data through deep learning

DR Kick, JG Wallace, JC Schnable… - G3: Genes …, 2023 - academic.oup.com
Accurate prediction of the phenotypic outcomes produced by different combinations of
genotypes, environments, and management interventions remains a key goal in biology with …

Rapid identification of high and low cadmium (Cd) accumulating rice cultivars using machine learning models with molecular markers and soil Cd levels as input data

Z Tang, TT You, YF Li, ZX Tang, MQ Bao, G Dong… - Environmental …, 2023 - Elsevier
Excessive accumulation of cadmium (Cd) in rice grains threatens food safety and human
health. Growing low Cd accumulating rice cultivars is an effective approach to produce low …

Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models

SA Tolley, LF Brito, DR Wang, MR Tuinstra - Frontiers in Genetics, 2023 - frontiersin.org
Genotype-by-environment interaction (GEI) is among the greatest challenges for maize
breeding programs. Strong GEI limits both the prediction of genotype performance across …

Maize yield prediction using artificial neural networks based on a trial network dataset

PVD de Souza, LP de Rezende, AP Duarte… - … , Technology & Applied …, 2023 - etasr.com
The prediction of grain yield is important for sowing, cultivar positioning, crop management,
and public policy. This study aims to predict maize productivity by applying an artificial …

Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics‐assisted …

JA Bhat, X Feng, ZA Mir, A Raina… - Physiologia …, 2023 - Wiley Online Library
Given the challenges of population growth and climate change, there is an urgent need to
expedite the development of high‐yielding stress‐tolerant crop cultivars. While traditional …

Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP

PG Heilmann, M Frisch, A Abbadi, T Kox… - Frontiers in Plant …, 2023 - frontiersin.org
Testcross factorials in newly established hybrid breeding programs are often highly
unbalanced, incomplete, and characterized by predominance of special combining ability …