Abstract
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.
Highlights
The variable of leaf wetness is understood as the presence of water on plant tissues [1]. It is measured as leaf water duration (LWD), which is defined as the time the plant surface shows visible water [2]
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Our results demonstrate that for the LWD modeling, it was not suitable to aggregate the records at a daily level because of the models’ worsened performance
Summary
The variable of leaf wetness is understood as the presence of water on plant tissues [1] It is measured as leaf water duration (LWD), which is defined as the time the plant surface shows visible water [2]. Diseases are one of the main factors that cause yield losses, and their development is directly associated with weather conditions that vary from one year to the next. Diseases are one of the main factors that cause yield losses, and their development is directly associated with weather conditions that vary from one year to the In this context, leaf wetness measurement supports the prevention and control strategies that guarantee a successful coffee production each year
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