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Volume 15
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Correction to Plants 2023, 12(24), 4142.
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Correction

Correction: Damásio et al. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. Plants 2023, 12, 4142

by
Miguel Damásio
1,4,*,
Miguel Barbosa
2,
João Deus
1,
Eduardo Fernandes
2,
André Leitão
2,
Luís Albino
2,
Filipe Fonseca
2 and
José Silvestre
1,3
1
INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal
2
SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal
3
GREEN-IT Bioresources4sustainability, ITQB NOVA, Av. da República, 2780-157 Oeiras, Portugal
4
GI-1716 Projects and Planification, Agroforestry Engineering Department, Escuela Politécnica Superior de Ingeniería Lugo, University of Santiago de Compostela, 27002 Lugo, Spain
*
Author to whom correspondence should be addressed.
Plants 2026, 15(12), 1764; https://doi.org/10.3390/plants15121764
Submission received: 26 May 2026 / Accepted: 27 May 2026 / Published: 8 June 2026
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
Versions Notes

Additional Affiliation

In the published publication [1], there was an error regarding the affiliation for Miguel Damásio. In addition to affiliation 1, the updated affiliation should include GI-1716 Projects and Planification, Agroforestry Engineering Department, Escuela Politécnica Superior de Ingeniería Lugo, University of Santiago de Compostela, 27002 Lugo, Spain. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Damásio, M.; Barbosa, M.; Deus, J.; Fernandes, E.; Leitão, A.; Albino, L.; Fonseca, F.; Silvestre, J. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. Plants 2023, 12, 4142. [Google Scholar] [CrossRef] [PubMed]
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MDPI and ACS Style

Damásio, M.; Barbosa, M.; Deus, J.; Fernandes, E.; Leitão, A.; Albino, L.; Fonseca, F.; Silvestre, J. Correction: Damásio et al. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. Plants 2023, 12, 4142. Plants 2026, 15, 1764. https://doi.org/10.3390/plants15121764

AMA Style

Damásio M, Barbosa M, Deus J, Fernandes E, Leitão A, Albino L, Fonseca F, Silvestre J. Correction: Damásio et al. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. Plants 2023, 12, 4142. Plants. 2026; 15(12):1764. https://doi.org/10.3390/plants15121764

Chicago/Turabian Style

Damásio, Miguel, Miguel Barbosa, João Deus, Eduardo Fernandes, André Leitão, Luís Albino, Filipe Fonseca, and José Silvestre. 2026. "Correction: Damásio et al. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. Plants 2023, 12, 4142" Plants 15, no. 12: 1764. https://doi.org/10.3390/plants15121764

APA Style

Damásio, M., Barbosa, M., Deus, J., Fernandes, E., Leitão, A., Albino, L., Fonseca, F., & Silvestre, J. (2026). Correction: Damásio et al. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. Plants 2023, 12, 4142. Plants, 15(12), 1764. https://doi.org/10.3390/plants15121764

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