| 000 | 02822nab|a22004457a|4500 | ||
|---|---|---|---|
| 001 | 67958 | ||
| 003 | MX-TxCIM | ||
| 005 | 20241122154231.0 | ||
| 008 | 202410s2024||||ne |||p|op||||00||0|eng|d | ||
| 022 | _a0168-9525 | ||
| 022 | _a1362-4555 (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.1016/j.tig.2024.07.001 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aFarooq, M.A. _914254 |
|
| 245 | 1 | 0 | _aArtificial intelligence in plant breeding |
| 260 |
_bNetherlands : _cElsevier B.V., _a2024. |
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| 500 | _aPeer review | ||
| 500 | _aOpen access | ||
| 520 | _aHarnessing 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. | ||
| 546 | _aText in English | ||
| 591 | _aAwais Rasheed : No CIMMYT Affiliation | ||
| 650 | 7 |
_aArtificial intelligence _917800 _2AGROVOC |
|
| 650 | 7 |
_aBig data _931310 _2AGROVOC |
|
| 650 | 7 |
_aGenetic gain _92091 _2AGROVOC |
|
| 650 | 7 |
_aPlant breeding _91203 _2AGROVOC |
|
| 700 | 0 |
_aShang Gao _937239 |
|
| 700 | 1 |
_aHassan, M.A. _97723 |
|
| 700 | 0 |
_aZhangping Huang _937241 |
|
| 700 | 1 |
_aAwais Rasheed _gGlobal Wheat Program _8I1706474 _91938 |
|
| 700 | 1 |
_aHearne, S. _gGenetic Resources Program _8INT3287 _9912 |
|
| 700 | 1 |
_aPrasanna, B.M. _gGlobal Maize Program _8INT3057 _9887 |
|
| 700 | 0 |
_aXinhai Li _94207 |
|
| 700 | 0 |
_aHuihui Li _gGenetic Resources Program _8CLIH01 _9764 |
|
| 773 | 0 |
_dNetherlands : Elsevier B.V., 2024. _gv. 40, no. 10, p. 891-908 _tTrends in Genetics _wG445736 _x0168-9525 |
|
| 856 | 4 |
_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35038 |
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| 942 |
_cJA _n0 _2ddc |
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| 999 |
_c67958 _d67950 |
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