# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 1 Introduction  
Downy mildew is among the most harmful diseases to grapevine [1, 2]. It is caused by the fungi _Plasmopara viticola_. The fungi overwinter in infected leaves and litter in the soil as oosopores that germinate in the spring and produce a sporangin. Rainfall and wind can disperse the primary zoospores to grapevine leaves. Zoospores can swim through water and only penetrate through the stomata. The process of sporangia production occurs at night (for at least 4 h) under very humid conditions (relative humidity of 95 to 100%) and at a favorable temperature (an optimal temperature for growth is approximately 25 \({}^{\circ}\)C). The number of infestations varies from one year to the next, primarily depending on the weather conditions. Under favorable weather conditions, the disease can cause huge losses due to the infection of the entire grapevine canopy (green parts). The worst epidemics occur in years with wet winters and wet springs followed by a hot summer with occasional rain showers. Meteorological parameters such as temperature, precipitation and relative humidity should be known in order to take proper measures against the disease. These parameters are used to determine the incubation period, which allows immediate actions to be taken. _Plasmopara viticola_ has two cycles of infestation. The first infestation cycle develops following the widely known 3-10 (three tens) rule [3]. The rule is based on the simultaneous occurrence of the following conditions: vine shoots at least 10 cm long, a minimum of 10 mm of rainfall within 24-48 h and average daily air temperature equal to or greater than 10 \({}^{\circ}\)C. Persistent rain, high humidity, and average daily temperatures higher than 11-14 \({}^{\circ}\)C (depending on region and authors) are critical to primary infections cycles. Secondary infections only occur if the primary infection has already spread. Primary infection spreadis directly observed in the form of the so-called \"oil stains\" found on grapevine leaves. Secondary infection can only occur under favorable night conditions, i.e., in humid, wet, and wind conditions, with temperatures higher than 12 \({}^{\circ}\)C. Various simulation models can provide the basis for the development of useful systems for timely disease warning and forecasting [2]. The winemaker can therefore optimize the number and timing of treatments. It is generally assumed that under favorable conditions, _Plasmopara viticola_ can inoculate one primary infection and several secondary infections. However, [PERSON] et al. [4] have shown that multiple primary infections can occur during a single season. The disease prevention strategies include treating the primary infection immediately before rainfall. A good weather forecast service is required in order to take as timely action as possible. If the infection has already occurred, a percentage of affected area will influence the method of treatment. Most of present disease treatment strategies include applying proper chemical sprays (copper-based fungicides or systematic fungicides, etc.). The application of sprays generally follows a fixed calendar schedule which results in a number of unnecessary treatments and large associated economic, health and environmental impacts. A more up-to-date approach in precision agriculture involves using precise data on environmental conditions (for example, on weather, soil or plants) and predicting infection periods. Precise data can enable the provision of timely warnings for both primary and secondary infections. A new model of the warning system with additional parameters that can replace conventional models is proposed in this paper. Wireless sensor networks (WSNs) have been used in short-range communication for decades. In the last decade, these have evolved into new systems such as massive machine to machine (M2M) and the Internet of Things (IoT) [5]. New technologies have recently emerged for scenarios where low power and a wide area are required.