Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases-Reference-Cited by-同舟云学术

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Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases

Published:2020-01-07 Issue:4 Volume:64 Page:671-688
ISSN:0020-7128
Container-title:International Journal of Biometeorology
language:en
Short-container-title:Int J Biometeorol

Author:

de Oliveira Aparecido Lucas Eduardo,de Souza Rolim Glauco,da Silva Cabral De Moraes Jose Reinaldo,Costa Cicero Teixeira Silva,de Souza Paulo Sergio

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Atmospheric Science,Ecology

Link

http://link.springer.com/content/pdf/10.1007/s00484-019-01856-1.pdf

Reference62 articles.

1. Androcioli HG, Hoshino AT, Menezes Júnior AO, Morais H, Bianco R (2018) Coffee leaf miner incidence and its predation bay wasp in coffee intercropped with rubber trees. Coffee Sci 13(3):389–400

2. Aparecido LEO, Rolim GS (2018) Forecasting of the annual yield of Arabic coffee using water deficiency. Pesq Agropec Bras 53(12):1299–1310

3. Avelino J; Cristancho M; Georgiou S; Imbach P; Aguilar L 2015 The coffee rust crises in Colombia and Central America (2008–2013): impacts, plausible causes and proposed solutions. : 303–321

4. Badnakhe MR, Durbha SS, Jagarlapudi A, Gade RM (2018) Evaluation of Citrus Gummosis disease dynamics and predictions with weather and inversion based leaf optical model. Comput Electron Agric 155:130–141

5. Benali L, Notton G, Fouilloy A, Voyant C, Dizene R (2019) Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renew Energy 132(2019):871–884

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