Prediction of the apple scab using machine learning and simple weather stations

Abstract

Apple scab is an economically important pest for apple production. It is controlled by applying fungicides when conditions are ripe for the development of its spores. This occurs when leaves are wet for a long enough time at a given temperature. However, leaf wetness is not a sufficiently well-defined agro-meteorological variable. Moreover, the readings of leaf wetness sensors depend to a large extent on their location within the tree canopy. Here we show that virtual wetness sensors, which are based on the easily obtained meteorological parameters such as temperature, relative humidity and wind speed, can be used in place of physical sensors. To this end, we have first collected data for two growing seasons from two types of wetness sensors planted in four locations in the tree canopy. Then, for each sensor we have built a machine-learning model of leaf wetness using the aforementioned meteorological variables. These models were further used as virtual sensors. Finally, Mills models of apple scab infection were built using both real and virtual sensors and their results were compared.The comparison of apple scab models based on real sensors shows significant variability. In particular, the results of a model depend on the location of the sensor within the canopy. The models based on data obtained from virtual sensors, are similar to the models based on physical sensors. Both types of models generate results within the same range of variability. The outcome of the study shows that the control of apple scab can be based on machine learning models based on standard meteorological variables. These variables can be readily obtained using inexpensive meteorological stations equipped with basic sensors. These results open the way to a widespread application of precise control of apple scab and consequently significant reduction of the use of pesticides in apple production with benefits for environment, human health and economics of production.