## 4 Discussion  
The poor quality of water-sensitive papers is crucial to discuss post-processing phase. In Figure 7 it is prominent the different legibility of reference cards and different WSP \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Treatments**} & \multicolumn{2}{c}{**GPM**} & \multicolumn{2}{c}{**GDM**} \\  & \multicolumn{2}{c}{**23 July 2021**} & \multicolumn{2}{c}{**10 September 2021**} \\ \hline  & **Incidence (\%)** & **Severity (1-7)** & **McKinney Index (\%)** & **Incidence (\%)** & **Severity (1-7)** & **McKinney Index (\%)** \\ \hline Untreated control & 1.72 \(\pm\) 4.40 a & 1.83 \(\pm\) 1.33 a & 0.47 \(\pm\) 1.51 a & 9.34 \(\pm\) 13.13 a & 1.80 \(\pm\) 1.01 a & 2.58 \(\pm\) 3.91 ab \\ LiDAR & 2.11 \(\pm\) 4.56 a & 1.33 \(\pm\) 0.71 b & 0.40 \(\pm\) 0.95 a & 3.81 \(\pm\) 7.26 b & 2.12 \(\pm\) 1.42 a & 0.98 \(\pm\) 1.76 b \\ Conventional sprayer & 1.36 \(\pm\) 7.70 a & 1.00 \(\pm\) 0.00 c & 0.19 \(\pm\) 1.10 a & 11.21 \(\pm\) 15.31 a & 2.00 \(\pm\) 1.19 a & 3.21 \(\pm\) 4.92 a \\ \hline \hline \end{tabular} Data are reported with means \(\pm\) standard deviation. Data followed by different letters in the same column and on the same date of assessment are significantly different according to [PERSON]’s LSD test (with \(p\leq 0.05\)).  
\end{table} 
Table 7: Details of grapevine powdery mildew (GPM) and grapevine downy mildew (GDM) infections recorded on grapevine bunches in Arcevia vineyard, respectively, on 23 July 2021 and 10 September 2021. Figure 9: Meteorological trend during 2021 in Arcevia. Data were obtained from the weather station of Arcevia (AN), 295 m above sea level and recorded from April to September. All the data were provided by the _Notiziario Agrometeorologico_, published by _Agrometeoro_ ASSAM, Marche Region, Italy. used. The former has a good contrast with the background, and it is possible to determine treatment distribution features through digital protocol; the latter has not enough contrast and it is possible to discriminate impact areas thanks only to human eyes capabilities. This issue affects data analysis because Inkscape was unable to properly generate closed lines or semi-closed lines due to the lack of difference between background and treatment impact. Consequently, QGs was unable to recognize closed lines and translate them into polygons. For these reasons, the comparing protocol has been adopted. The comparing protocol has been improved starting from [PERSON] et al. protocol, based on provided WSP samples by the chemical company. As has been said above, the cited paper did not provide any methodology on how WSP references were constructed; consequently, the present work has developed an objective and replicable reference method. The updated comparing protocol proved to be positive in use. The first purpose of the work was to evaluate the effectiveness of laser scanner system from mechanical point of view. Results show increased frequency in 10-15% coverage range for LiDAR tests, coherent with the sensors principles, and increased frequency in over 20% coverage range for standard tests, because the distribution of agrochemicals is steady throughout the row. The conclusion is that smart sprayer is able to distinguish between vines plants and vines gaps along the row. The frequency distribution of the coverage classes has been analyzed through a plot (Figure 10) and it is clear that there are two different trends:  
* The frequency of LiDAR vines gaps has a bell-shaped distribution, with the maximum frequency around 10-15%, and the variability observed with smart sprayer maybe depends on the variability of gaps size (small gaps are harder to spot than very large gaps, so it means that more agrochemicals is distributed on small gaps). * The frequency of Standard vines gaps reflects an exponential trend, considering that the last coverage classes is \"over 20%\". The last point is important to introduce another discussion about the coverage area overlap.