# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 4 Discussion
However, the pattern of differences between the spectra of the two groups vanished when the NIRScan spectra were observed; this might be due to the fact that the physiological changes in relation to a reduction of leaf water content do not vary during the early stage of infection. Further, it is hard to tell if the spectral differences between healthy and diseased leaves will increase with the increase in infection throughout the vegetative growth stages. In fact, the grapevine leaves responded in different manners in the later growth stages. The third and fourth-week healthy leaves showed higher reflectance values for the Hamamatsu sensor; but, a week later, in the fifth week, the opposite pattern was observed in the spectra most likely due to other symptoms from other several chronic diseases that can affect the spectral data. Somewhat similar alterations affecting chlorophyll content and different pathways in the secondary metabolism are also found in response to other biotic and abiotic stressors, such as virus-like symptoms, esca dieback, or drought stress. However, more detailed analyses of different diseases may reveal peculiar metabolic changes possibly associated with reflectance differences and deserve further investigation [54, 55, 56]. In fact, comparative reports are lacking about the physiological alterations caused by different stressors on the same variety, in a time course along the growing season and the disease progression. The results of this study suggest that even in a complex phytopathological scenario, the reflectance within the VIS-NIR spectral range is very promising to detect phytoplasma-infected plants. The confusion matrix (Figure 9) shows the overall scenario of the best classifier (LR) where we can observe that from the 13 infected none are resulting as \"False negatives\" and two samples (11.11%) are classified as \"False positives\" when using the test sample (18 samples). In case of LOO cross-validation, out of 60 samples, only 4 samples (6.67%) were wrongly classified as \"False negatives\" and 5 samples (8.33%) were classified as \"False positive\". In fact, from the analysis of the confusion matrix, it is more beneficial for the end-user farmers to have fewer \"False negatives\" with respect to \"False positives\". This allows the identification of the source of the FD outbreak that might affect the healthy plants, thus providing higher chances to prevent disease spread in neighboring vineyards. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dates** & **Sensors** & **Features** & **Classifiers** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1 Score (\%)** \\ \hline
1 July 2022 & Hamamatsu & GA (13) & LR & 89 & 100 & 85 & 92 \\
21 June 2022 & NIRScan & GA (7) & RF & 72 & 91 & 71 & 80 \\
1 July 2022 & Joined spectra & GA (7) & SVM, LR & 83 & 91 & 83 & 87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of best accuracies achieved on test samples, full spectra, ensemble method (Ens method), and genetic algorithms (GA). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dates** & **Sensors** & **Features** & **Classifiers** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1 Score (\%)** \\ \hline
1 July 2022 & Hamamatsu & Ens method (100) & SVM & 85 & 88 & 85 & 87 \\
21 June 2022 & NIRScan & GA (3) & GB & 68 & 88 & 66 & 75 \\
1 July 2022 & Joined spectra & Ens method (100) & RF & 77 & 74 & 88 & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of best accuracies achieved on whole dataset with leave-one-out (LOO) cross-validation, full spectra, ensemble method (Ens method), and genetic algorithms (GA). The present study is based on a small dataset and conducted on a single grapevine variety. The further validation of our results will require the collection of a larger dataset (especially from asymptomatic samples) from commercial vineyards in other growing seasons and other varieties.