# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 4 Discussion  
The majority of models used for the downy mildew early warning part of the system are based on incubation methods. The purpose of incubation methods is to determine the duration of the incubation period in days (usually between 4 and 15 days). These methods usually use some common rule for the start of the incubation period (e.g., 3-10 s rule). Most of the used methods are based on temperature as the only parameter used to forecast the duration of the incubation period. An overview of these methods is given in the related work subsection (e.g., [14; 34; 35]). The first improvement over the temperature-based incubation methods is the introduction of humidity as a parameter to the model. One such example is the Goidanich index (GI) [16] which introduced two categories of humidity--low and high. The GI combined with aerobiological data was recently assessed [36]. Another example of adding humidity is given in mechanistic model 1 [18] where the duration of primary inoculum season is measured based on so-called hydro-thermal time. The iMetos(r) system [13] offers a widespread, commercial monitoring solution with hourly data sampling [37]. Sporangia development is based on the germination of overwintered oospores in leaf litter on the ground. Appropriate weather conditions are necessary for germination to start. Rainfall on current and previous days has the most effect on spore concentration [36]. The most important weather parameter is the total amount of rainfall during the month of May [28]. In addition, the wind intensity is also important for sporangia dispersion [20]. Although the statistical analyses [36] demonstrated that the wind is not one of the main parameters influencing infection risk, we strongly believe that this claim needs to be more thoroughly investigated. Our Winet model is an equation and algorithm-based model. It includes both primary and secondary alarms, has hourly monitoring and average daily data, uses air temperature, humidity, rainfall, leaf wetness (LW), day/night time, and wind speed data from sensors. As demonstrated in the Related Work section, the majority of available models from the literature are missing some parameters (e.g., all incubation methods). Mechanistic model 2 (for secondary alarms only and without the wind parameter) and iMetos(r) system are the only ones closely related to the Winet model. The cross-comparison of features among Winet and other models from the literature is presented in Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RS**} & \multicolumn{2}{c}{**VV**} & \multicolumn{2}{c}{**T**} \\ \cline{2-7}  & **P** & **S** & **P** & **S** & **P** & **S** \\ \hline W/iMl & 4/8 & 4/8 & 10/11 & 14/23 & 13/13 & 12/18 \\ W/iMm & 2/5 & 3/4 & 9/10 & 13/14 & 9/9 & 4/4 \\ W/iMs & 2/2 & 2/2 & 8/8 & 12/12 & 4/4 & 3/3 \\ \hline W+ & 4 & 3 & 2 & 0 & 5 & 4 \\ \hline Cl & 0.5 & 0.5 & 0.91 & 0.61 & 1 & 0.67 \\ Cm & 0.4 & 0.75 & 0.9 & 0.93 & 1 & 1 \\ Cs & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline W/M2 & - & 5/7 & - & 15/21 & - & 15/27 \\ \hline W+ & - & 2 & - & 1 & - & 0 \\ \hline CM2 & - & 0.71 & - & 0.71 & - & 0.55 \\ \hline \hline \end{tabular} Legend: RS—Rimski Sänevri’; VV—Vršaki Vinogradi’; T—Trient’; P—primary infection; S—secondary infection; W—Winet; iMl—iMetos® light; iMm—iMetos® moderate; iMs—Metos® severe; W+—additional Winet alarms; Cl/Cm/Cs—correlation of W and iMl/iMm/iMs; M2—mechanistic model 2 [20]; CM2—correlation of W and M2.  
\end{table} 
Table 3: Winet vs. iMetos® and mechanistic 2 alarm comparisons. Primary infection alarm comparisons between Winet and iMetos(r) [13] from Figures 13-15, show that Winet fired 4 out of 8, 10 out of 11, and 13 out of 13 iMetos(r) light alarms for the RS, VV, and T locations, respectively. These correspond to correlations of 50%, 91%, and 100%, respectively. Moderate alarms show similar results: 2 out of 5, 9 out of 10, and 9 out of 9 with corresponding correlations of 40%, 90%, and 100%, respectively.