Machine Learning Methods for Leaf Wetness Prediction using Agrometeorological Data

This paper addresses the estimation of leaf wetness by means of machine learning methods applied to agrometeorological data. The novel contribution lies in the application of the models to the top and bottom sides of the leaf, separately. For this purpose, data have been acquired in the field using weather stations equipped with several sensors: double-sided capacitive leaf wetness, air temperature and relative humidity, soil temperature and volumetric water content, wind speed and rainfall. Leaf wetness measurements have been used as ground truth, whereas the other sensor data as features. Results demonstrate the applicability of the method, which has also been tested on edge. The dataset has been made publicly available under a Creative Commons license.