| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Uva e Vinho. |
|
Data corrente: |
20/12/2023 |
|
Data da última atualização: |
20/12/2023 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
MAGRO, R. B.; ALVES, S. A. M.; GEBLER, L. |
|
Afiliação: |
RENATA BULLING MAGRO, EMBRAPA UVA E VINHO; SILVIO ANDRE MEIRELLES ALVES, CNPUV; LUCIANO GEBLER, CNPUV. |
|
Título: |
Computational models in precision fruit growing: reviewing the impact of temporal variability on perennial crop yield assessment. |
|
Ano de publicação: |
2023 |
|
Fonte/Imprenta: |
SN Computer Science, v. 4, n. 554, p. 1-13, 2023. |
|
DOI: |
https://doi.org/10.1007/s42979-023-02103-6 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Early yield information of perennial crops is crucial for growers and the industry as it enables cost reduction and facilitates rop planning. However, assessing the yield of perennial crops using computational models poses challenges due to the diverse aspects of interannual variability that afect these crops. This review aimed to investigate and analyze the literature on yield estimation and forecasting modeling in perennial cropping systems. We reviewed 49 articles and categorized them according to their yield assessment strategy, modeling class, and input variable characteristics. The strategies of yield assessment were discussed in the context of their principal improvement challenges. Our investigation revealed that image processing and deep learning models are emerging techniques for yield estimation. On the other hand, machine learning algorithms, such as Artifcial Neural Networks and Decision Trees, were applied to yield forecasting with reasonable time in advance of harvest. Emphasis is placed on the lack of representative long-term datasets for developing computational models, which can lead to accurate early yield forecasting of perennial crops. |
|
Palavras-Chave: |
Computational intelligence; Decision support; Machine learning; Spatio-temporal analysis; Yield modeling. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1160091/1/Magro-SNComputerScience-4554-2023.pdf
|
|
Marc: |
LEADER 01902naa a2200217 a 4500
001 2160091
005 2023-12-20
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s42979-023-02103-6$2DOI
100 1 $aMAGRO, R. B.
245 $aComputational models in precision fruit growing$breviewing the impact of temporal variability on perennial crop yield assessment.$h[electronic resource]
260 $c2023
520 $aEarly yield information of perennial crops is crucial for growers and the industry as it enables cost reduction and facilitates rop planning. However, assessing the yield of perennial crops using computational models poses challenges due to the diverse aspects of interannual variability that afect these crops. This review aimed to investigate and analyze the literature on yield estimation and forecasting modeling in perennial cropping systems. We reviewed 49 articles and categorized them according to their yield assessment strategy, modeling class, and input variable characteristics. The strategies of yield assessment were discussed in the context of their principal improvement challenges. Our investigation revealed that image processing and deep learning models are emerging techniques for yield estimation. On the other hand, machine learning algorithms, such as Artifcial Neural Networks and Decision Trees, were applied to yield forecasting with reasonable time in advance of harvest. Emphasis is placed on the lack of representative long-term datasets for developing computational models, which can lead to accurate early yield forecasting of perennial crops.
653 $aComputational intelligence
653 $aDecision support
653 $aMachine learning
653 $aSpatio-temporal analysis
653 $aYield modeling
700 1 $aALVES, S. A. M.
700 1 $aGEBLER, L.
773 $tSN Computer Science$gv. 4, n. 554, p. 1-13, 2023.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Uva e Vinho (CNPUV) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 125 | |
| 15. |  | ALVES, S. A. M.; NUNES, C. C. Avanços no manejo do cancro europeu em macieira. In: ENCONTRO NACIONAL SOBRE FRUTICULTURA DE CLIMA TEMPERADO, 16., 2019, Fraiburgo, SC. Anais...Levando conhecimento e tecnologia para a fruticultura. Vol I - Palestras. Fraiburgo, SC: Epagri, 23 a 25 jul. 2019. p. 43-47. XVI ENFRUTE 2019| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Uva e Vinho. |
|    |
| Registros recuperados : 125 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
