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

Autor:	Ivan Mezei, Lazar Berbakov, Bogdan Pavkovic, Boris Radovanović, Milan Lukic
Rok vydání:	2022
Předmět:	energy harvesting early warning decision support TK7800-8360 Computer Networks and Communications Hardware and Architecture Control and Systems Engineering NB-IoT Signal Processing Electronics Electrical and Electronic Engineering grapevine downy mildew
Zdroj:	Electronics; Volume 11; Issue 3; Pages: 356 Electronics, Vol 11, Iss 356, p 356 (2022)
ISSN:	2079-9292
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f64d7d54b4d648e4194173ebf45d43e https://doi.org/10.3390/electronics11030356 Zobrazit plný text záznamu