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| Registros recuperados : 6 | |
| 4. |  | GULLINO, M. L.; ALBALES, R.; AL-JBOORY, I.; ANGELOTTI, F.; CHAKRABORTY, S.; GARRETT, K. A.; HURLEY, B. P.; JUROSZEK, P.; MAKKOUK, K.; STEPHENSON, T. Scientific review of the impact of climate change on plant pests: a global challenge to prevent and mitigate plant pest risks in agriculture, forestry and ecosystems. Rome: FAO: IPCC, 2021. 72 p. il.| Biblioteca(s): Embrapa Semiárido. |
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| 5. | | GULLINO, M. L.; ALBAJES, R.; AL JBOORY, I.; ANGELOTTI, F.; CHAKRABORTY, S.; GARRETT, K. A.; HURLEY, B. P.; JUROSZEK, P.; MAKKOUK, K.; PAN, X.; STEPHENSON, T. Summary for policymakers of the scientific review of the impact of climate change on plant pests: a global challenge to prevent and mitigate plant pest risks in agriculture, forestry and ecosystems. Rome: FAO: IPPC, 2021. 32 p. il.| Biblioteca(s): Embrapa Semiárido. |
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| 6. | | GULLINO, M. L.; ABAJES, R.; AL-JBOORY, I.; ANGELOTTI, F.; CHAKRABORTY, S.; GARRETT, K. A.; HURLEY, B. P.; JUROSZEK, P.; LOPIAN, R.; MAKKOUK, K.; PAN, X.; PUGLIESE, M.; STEPHENSON, T. Climate change and pathways used by pests as challenges to plant health in agriculture and forestry. Sustainability, v. 14, 12421, 2022.| Biblioteca(s): Embrapa Semiárido. |
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| Registros recuperados : 6 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
07/02/2023 |
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Data da última atualização: |
15/06/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
B - 4 |
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Autoria: |
VERSLYPEA, N. I.; NASCIMENTO, A. C. A. do; MUSSER, R. dos S.; CALDASM R, M. de S.; MARTINS, L. S. S.; LEAO, P. C. de S. |
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Afiliação: |
NINA IRIS VERSLYPEA, UFRPE; ANDRÉ CÂMARA ALVES DO NASCIMENTO, UFRPE; ROSIMAR DOS SANTOS MUSSER, UFRPE; RAPHAEL MILLER DE SOUZA CALDAS, UFRPE; LUIZA SUELY SEMEN MARTINS, UFRPE; PATRICIA COELHO DE SOUZA LEAO, CPATSA. |
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Título: |
Drought tolerance classification of grapevine rootstock by machine learning for the São Francisco Valley. |
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Ano de publicação: |
2023 |
|
Fonte/Imprenta: |
Smart Agricultural Technology, v. 4, 100192, 2023. |
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DOI: |
https://doi.org/10.1016/j.atech.2023.100192 |
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Idioma: |
Inglês |
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Conteúdo: |
Machine Learning (ML) algorithms are increasingly being used in several areas of agricultural studies, such as plant breeding. ML can assist in the recognition of relevant patterns or groups, or even in the prediction of the outcome under new settings, thus accelerating experiments and interpretating their results. The identification and selection of drought-tolerant grapevine rootstock (Vitis spp.) have become more relevant in late years, motivated mostly by global climate change scenarios. However, the grapevine is a perennial species, with polygenic characteristics and a complex traits inheritance by offspring, thus making it very challenging to discover new, drought tolerant cultivars. For this reason, this study's main objective was to compare the performance of six machine learning models on the prediction of drought tolerance levels of grapevine rootstock cultivars. A dataset with forty-five distinct cultivars was used to evaluate the methods, and the best performing model (AUC 0.9857) was used to predict the drought tolerance class of three cultivars (IAC 313, IAC 572, and IAC 766) whose drought tolerance level was still unknown. The results predicted a high drought tolerance for IAC 313 and IAC 766 cultivars, and a low tolerance for IAC 572. |
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Palavras-Chave: |
Algoritmo; Aprendizado supervisionado; Inteligência artificial; Vale de São Francisco. |
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Thesagro: |
Mudança Climática; Porta Enxerto; Uva. |
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Thesaurus NAL: |
Algorithms; Artificial intelligence; Climate change; Grapes. |
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Categoria do assunto: |
A Sistemas de Cultivo |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/251869/1/Drought-tolerance-classification-of-grapevine-rootstock-by-machine-learning-2023.pdf
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Marc: |
LEADER 02275naa a2200325 a 4500
001 2151568
005 2023-06-15
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.atech.2023.100192$2DOI
100 1 $aVERSLYPEA, N. I.
245 $aDrought tolerance classification of grapevine rootstock by machine learning for the São Francisco Valley.$h[electronic resource]
260 $c2023
520 $aMachine Learning (ML) algorithms are increasingly being used in several areas of agricultural studies, such as plant breeding. ML can assist in the recognition of relevant patterns or groups, or even in the prediction of the outcome under new settings, thus accelerating experiments and interpretating their results. The identification and selection of drought-tolerant grapevine rootstock (Vitis spp.) have become more relevant in late years, motivated mostly by global climate change scenarios. However, the grapevine is a perennial species, with polygenic characteristics and a complex traits inheritance by offspring, thus making it very challenging to discover new, drought tolerant cultivars. For this reason, this study's main objective was to compare the performance of six machine learning models on the prediction of drought tolerance levels of grapevine rootstock cultivars. A dataset with forty-five distinct cultivars was used to evaluate the methods, and the best performing model (AUC 0.9857) was used to predict the drought tolerance class of three cultivars (IAC 313, IAC 572, and IAC 766) whose drought tolerance level was still unknown. The results predicted a high drought tolerance for IAC 313 and IAC 766 cultivars, and a low tolerance for IAC 572.
650 $aAlgorithms
650 $aArtificial intelligence
650 $aClimate change
650 $aGrapes
650 $aMudança Climática
650 $aPorta Enxerto
650 $aUva
653 $aAlgoritmo
653 $aAprendizado supervisionado
653 $aInteligência artificial
653 $aVale de São Francisco
700 1 $aNASCIMENTO, A. C. A. do
700 1 $aMUSSER, R. dos S.
700 1 $aCALDASM R, M. de S.
700 1 $aMARTINS, L. S. S.
700 1 $aLEAO, P. C. de S.
773 $tSmart Agricultural Technology$gv. 4, 100192, 2023.
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Embrapa Semiárido (CPATSA) |
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