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One major problem that affecting grape production is that of infestations by fungal pathogens, among which Plasmopara viticola is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting disease outbreak rely on the monitoring of meteorological parameters. With the recent development of Internet of Things (IoT) technologies, in situ data can be efficiently collected on a large scale. In this paper, a new model with early warning system implementation for grapevine downy mildew based on Narrow Band IoT (NB-IoT) and energy harvesting is presented. Models of downy mildew warning systems have evolved from the early temperature-based (and later, humidity-based) models to the latest mechanistic models which include rainfall/leaf wetness and hourly monitoring. We added parameters such as ’favorable night condition’ and ’wind speed’ as critical for sporangia spreading. The comparison of the model with the commercial iMetos® warning system and the latest mechanistic model for three specific vineyard locations indicates a high correlation between alarms.

Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology