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Article

Approaches for the Prediction of Leaf Wetness Duration with Machine Learning

1
Tecnológico de Costa Rica, Cartago 159-7050, Costa Rica
2
Instituto del Café de Costa Rica, Heredia 280-3011, Costa Rica
*
Author to whom correspondence should be addressed.
Academic Editors: Juan Luis Crespo-Mariño and Andrés Segura-Castillo
Biomimetics 2021, 6(2), 29; https://doi.org/10.3390/biomimetics6020029
Received: 1 March 2021 / Revised: 5 May 2021 / Accepted: 11 May 2021 / Published: 14 May 2021
(This article belongs to the Special Issue Bioinspired Intelligence II)
The prediction of leaf wetness duration (LWD) is an issue of interest for disease prevention in coffee plantations, forests, and other crops. This study analyzed different LWD prediction approaches using machine learning and meteorological and temporal variables as the models’ input. The information was collected through meteorological stations placed in coffee plantations in six different regions of Costa Rica, and the leaf wetness duration was measured by sensors installed in the same regions. The best prediction models had a mean absolute error of around 60 min per day. Our results demonstrate that for LWD modeling, it is not convenient to aggregate records at a daily level. The model performance was better when the records were collected at intervals of 15 min instead of 30 min. View Full-Text
Keywords: Leaf wetness duration; machine learning; coffee leaf Leaf wetness duration; machine learning; coffee leaf
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MDPI and ACS Style

Solís, M.; Rojas-Herrera, V. Approaches for the Prediction of Leaf Wetness Duration with Machine Learning. Biomimetics 2021, 6, 29. https://doi.org/10.3390/biomimetics6020029

AMA Style

Solís M, Rojas-Herrera V. Approaches for the Prediction of Leaf Wetness Duration with Machine Learning. Biomimetics. 2021; 6(2):29. https://doi.org/10.3390/biomimetics6020029

Chicago/Turabian Style

Solís, Martín, and Vanessa Rojas-Herrera. 2021. "Approaches for the Prediction of Leaf Wetness Duration with Machine Learning" Biomimetics 6, no. 2: 29. https://doi.org/10.3390/biomimetics6020029

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