# _remote sensing_
## 3 Results
### Crop Diseases Results
#### Orchards Diseases Mapping
The study on the New Zealand sites also included PRISMA hyperspectral imagery, which allowed adding a new set of VIs. The analysis was carried out on archived images of the areas that in 2019 were clearly identified as infected (Te Puke) and non-infected (South Island). A similar analysis, ongoing, aims at including a new set of PRISMA images, collected monthly, to assess the possibility for identifying the potential outbreaks at different phenological stages in the Agro Pontino region in Central Italy. To improve the performance of the NN, a new set of VIs based on hyperspectral imagery was added. The NN was designed with similar characteristics to the previous analysis (20 hidden layers, 60% training, 20% validation, and 20% testing). In this case, the performance of the classifier was significantly improved, reaching values greater than 95% (Tables 5 and 6). The same NN, trained using the data on New-Zealand, was saved and applied to the area devoted to the cultivation of kiwi located in the Lazio Region of Italy, where the presence of this p \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Confusion Matrix with 12 Initial** & **Confusion Matrix with Extended** \\  & **VIs** & **List of VIs** \\ \hline Output class 1 & 58.5\% & 4.8\% & 59.7\% & 2.5\% \\ Output class 2 & 3.1\% & 33.7\% & 1.8\% & 36.0\% \\  & Target class 1 & Target class 2 & Target class 1 & Target class 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between confusion matrices. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training Confusion Matrix**} & \multicolumn{3}{c}{**Validation Confusion Matrix**} \\ \hline Output class 1 & 59.5\% & 5.4\% & 91.7\% & 61.5\% & 5.4\% & 92.0\% \\ Output class 2 & 2.1\% & 33.1\% & 94.2\% & 5.4\% & 27.7\% & 83.7\% \\  & 96.7\% & 86.0\% & 92.6\% & 92.0\% & 83.7\% & 89.2\% \\  & Target class 1 & Target class 2 & OA & Target class 1 & Target class 2 & OA \\ \hline \hline \multicolumn{3}{c}{**Test Confusion Matrix**} & \multicolumn{3}{c}{**All Confusion Matrix**} \\ \hline Output class 1 & 52.3\% & 2.3\% & 95.8\% & 31.3\% & 6.0\% & 83.9\% \\ Output class 2 & 3.8\% & 41.5\% & 91.5\% & 5.5\% & 57.3\% & 91.2\% \\  & 93.2\% & 94.7\% & 93.8\% & 85.0\% & 90.5\% & 88.5\% \\  & Target class 1 & Target class 2 & OA & Target class 1 & Target class 2 & OA \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix result from the application of the NN classification procedure with the new dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training Confusion Matrix**} & \multicolumn{3}{c}{**Validation Confusion Matrix**} \\ \hline Output class 1 & 58.5\% & 5.4\% & 95.8\% & 63.1\% & 1.5\% & 97.6\% \\ Output class 2 & 2.3\% & 36.7\% & 94.1\% & 0.0\% & 35.4\% & 100\% \\  &  
The NN was then fed with VIs extracted using the Rapideye images of the Agro Pontino. It can be noticed that certain blocks (or maturity areas) show similar responses and were classified under different levels of probability of infection (Figure 10). Nevertheless, it is important to underline that the NN was trained considering only the presence of Psa (_Pseudomonas syringae_ pv. Actinidae) epidemics which have caused major economic losses to kiwifruit industry since 2007-2008, while, more recently, the _Halyomorpha halys_, also known as the brown marmorated stink bug, has seriously compromised fruit quality and marketable yields [46]). For the test cases located in Italy, it could not be concluded that Psa was present in the fields, for which no ground data were available, but orchards showed a similar behavior to the fields of other regions which were identified as infected by Psa. Therefore, in the Agro Pontino case study, further field campaigns have to be carried out for validation purpose. This would help to improve the robustness of the current NN. Despite the promising results as shown in Table 7, the performance of the NN for this case study could also be related to different phenology states of the orchards.