# _remote sensing_
## 3 Results
### Crop Diseases Results  
#### Orchards Diseases Mapping  
Having in mind the objective of assessing the possibility of monitoring the spread of diseases, Psa in this case, in permanent crops such as kiwi trees, by using EO satellite images, we focused on the analysis of RapidEye images to validate the results presented in [17]. In that work, multiple vegetation indices were generated and a binomial logistic regression was used to relate these vegetation indices to the presence of Psa. Psa data used in [17] were provided with some uncertainty about the actual infection date. In the present study, the data were reclassified, and the analysis was confined to KBIs (Kiwi Blocks identified by KVH) with either an 'early' (infected) or 'control' (non-infected) response. [PERSON] et al. [45] identified potential relationships between Psa infection and canopy vigour retrieved by remotely sensed imagery. However, they recognized that there were limitations in their study, particularly about the resolution at which the infection data was collected (orchard level rather than maturity level). Initially, the study was carried out using RapidEye images, and an approach similar (in terms of VIs and NN) to the one used to study the New Zealand's regions was adopted. Subsequently, we further investigated Sentinel-2 and PRISMA imagery by applying the above mentioned Vis. Figure 8: Simulated yield (with data assimilation) vs. observed yield. Concerning the analysis carried out on the regions of New Zealand based on the Rapid-Eye imagery of 2010 and 2012, the confusion matrix (Table 3) obtained by applying the NN shows an acceptable percentage of good classifications (AO higher than 69%). A way to measure how well the NN fits data is the receiver operating characteristic (ROC) plot. Figure 9 shows the relationship of false positive and true positive rates as the thresholding of outputs is varied from 0 to 1. The ROC curve shows the true positive rate versus false positive rate (equivalently, sensitivity versus 1-specificity) for different thresholds of the classifier output. The results obtained from the second analysis, which included the same list of VIs applied previously, now extracted from Sentinel-2 images (Table 4), were obtained considering the health status of the orchards published by the biosecurity organization KVH in 2019. These results showed an improvement in the performance, highlighting the potential use of Sentinel-2 datasets for the detection of Psa outbreaks. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Confusion Matrix**} & \multicolumn{2}{c}{**Validation Confusion Matrix**} \\ \hline Output class 1 & 36.3\% & 3.8\% & 90.6\% & 25.0\% & 8.8\% & 74.1\% \\ Output class 2 & 5.4\% & 54.6\% & 91.0\% & 5 & 48 & 90.6\% \\  & 87.0\% & 93.6\% & 85.0\% & 80.0\% & 87.3\% & 85.0\% \\  & Target class 1 & Target class 2 & AO & Target class 1 & Target class 2 & AO \\ \hline \multicolumn{5}{c}{**Test Confusion Matrix**} & \multicolumn{2}{c}{**All Confusion Matrix**} \\ \hline Output class 1 & 22.5\% & 10.0\% & 69.2\% & 31.3\% & 6.0\% & 83.9\% \\ Output class 2 & 5.0\% & 62.5\% & 92.6\% & 5.5\% & 57.3\% & 91.2\% \\  & 81.8\% & 86.2\% & 85.0\% & 85.0\% & 90.5\% & 88.5\% \\  & Target class 1 & Target class 2 & AO & Target class 1 & Target class 2 & AO \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix results from the application of the NN classification on RapidEye VI. Figure 9: ROC Curves showing the performance of the classificatory when applied to RapidEye images. Class 1 indicates infected blocks, while class 2 indicates blocks where the pest has not been detected. The datasets ingested into the NN included the statistics for each VI (as Maximum, Minimum, Mean and Standard deviation), for each block (or maturity area). After the addition of 250 blocks from Takaka region to the previously digitized 400 blocks from the Te Puke area, the classification performance improved, increasing the accuracy up to 95%.