Título:	Computational models in precision fruit growing: reviewing the impact of temporal variability on perennial crop yield assessment.
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.
Ano de publicação:	2023
Referência:	SN Computer Science, v. 4, n. 554, p. 1-13, 2023.
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:	Yield modeling Spatio-temporal analysis Computational intelligence Machine learning Decision support
Digital Object Identifier:	https://doi.org/10.1007/s42979-023-02103-6
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPUV)