TY  - JA
AU  - Farooq,M.A.
AU  - Shang Gao
AU  - Hassan,M.A.
AU  - Zhangping Huang
AU  - Awais Rasheed
AU  - Hearne,S.
AU  - Prasanna,B.M.
AU  - Xinhai Li
AU  - Huihui Li
TI  - Artificial intelligence in plant breeding
SN  - 0168-9525
PY  - 0000///Elsevier B.V.,
CY  - 2024.
PB  - Netherlands
KW  - Artificial intelligence
KW  - AGROVOC
KW  - Big data
KW  - Genetic gain
KW  - Plant breeding
N1  - Peer review; Open access
N2  - 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 scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype–phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field
UR  - https://hdl.handle.net/10883/35038
DO  - https://doi.org/10.1016/j.tig.2024.07.001
T2  - Trends in Genetics
ER  -