# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 5 Conclusions  
A grapevine downny mildew early warning system for both primary and secondary infections was presented in this paper. The proposed model was compared to the models \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **P/S** & \begin{tabular}{c} **h/avg** \\ **Data** \\ \end{tabular} & \begin{tabular}{c} **Air** \\ **Temperature** \\ \end{tabular} & \
\begin{tabular}{c} **Humidity** \\ \end{tabular} & **Rainfall** & **LW** &
\begin{tabular}{c} **Light** \\ **(Day/Night)** \\ \end{tabular} & **Wind** & **E/A** \\ \hline [PERSON] [14] & P & avg & ✓ & - & - & - & - & - & E \\ [PERSON] [34] & P & avg & ✓ & - & - & - & - & E \\ [PERSON]- & P & avg & ✓ & - & - & - & - & - & E \\ [PERSON] [35] & P & avg & ✓ & ✓ & - & - & - & - & E \\ [PERSON] [16] & P & avg & ✓ & ✓ & - & - & - & E \\ Mechanistic 1 [18] & P & h/avg & ✓ & ✓ & - & - & - & E \\ Mechanistic 2 [20] & S & h/avg & ✓ & ✓ & ✓ & ✓ & - & E+A \\ iMetos® [13] & P+S & h+avg & ✓ & ✓ & ✓ & ✓ &? & E+A \\ Our model & P+S & h+avg & ✓ & ✓ & ✓ & ✓ & ✓ &? & E+A \\ \hline \multicolumn{10}{c}{Legend: P—primary infection; S—secondary infection; h—hourly data; avg—averaged daily data; LW—leaf wretness; E—equation based; A—algorithm based.} \\ \end{tabular}
\end{table} 
Table 4: Features comparison of different models. described in literature, with certain applied modifications (e.g., favorable conditions at night for disease development) and additions (e.g., wind measurements) considered useful. The comparison of the results obtained in the proposed Winet early warning system for three vineyard locations (two in Serbia and one in Switzerland) and in commercially available iMetos(r) system indicates high correlation between the alarms (especially with alarms for moderate and severe infections). Comparisons between Winet and mechanistic model 2 [20] indicate a solid correlation. The proposed system features the new low-power NB-IoT communication and modules for energy harvesting based on photovoltaics. The system is designed to operate battery-free since the solar panel can fully compensate the daily power consumption. Winet system features both Web and mobile applications for user interactions, visualization, and alarms. The proposed system can provide a solid platform to help grape growers make decisions on the disease control strategies in order to optimize the schedule of the treatments, resulting in positive health, environmental, and economic effects. The first step in future work will be to validate the present findings with a large set of data (including data from a number of years). The proposed model can also be compared to some \"simpler\" models (e.g., [PERSON]'s). Then, a larger dataset will be used to feed an appropriate machine learning algorithm that calculates the risk of disease outbreak. It would be interesting to experiment with the proposed model in vivo to see whether any additional alerts, such as the one in Table 3 (W+), that are not provided by iMetos(r), can be valid in the next season. In addition, a more thorough investigation of the wind factor would be useful. At present, the proposed model can only forecast grapevine downy mildew. In the future, the Winet system may incorporate other disease models in order to enable the control of various diseases, not only in viticulture, but also in other areas of agriculture. System conceptualization, implementation, and tests, M.L., L.B., B.P. and I.M. ; model and methodology conceptualization, I.M. ; model implementation, I.M. and B.R. ; dataset analysis and model simulations, B.R. ; writing--original draft preparation, all authors; writing--review and editing, all authors; funding acquisition, I.M. All authors have read and agreed to the published version of the manuscript. The work of the first and second authors is partially supported by the H2020 INCOMING grant under number 856967. Not applicable. Data Availability Statement: Data could be provided upon request.