Approaches for the Prediction of Leaf Wetness Duration with Machine Learning.

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.

Plant phenotyping relevance

葉面濡れ時間という植物の状態をセンサーで測定し、機械学習による推定手法と時間分解能・性能を比較評価しており、表現型取得・推定法が研究の中心である。

Code and data availability

The paper's LWD/meteorological dataset from ICAFE stations in six Costa Rican coffee regions is paper-specific but only available upon request; no public code, models, or data deposits are stated.

No evidence-backed public reproduction asset is currently recorded.