# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 4 Discussion  
The vineyard selected in this study, planted in 2005, was in generally poor phytosanitary conditions, with disetaneous vines, due to multiple replacements of old/dead plants along the years (26% of plants less than 5 years old, 20% more than 15 years, about half of the plants between 5 and 15 years old). The vineyard was affected by several chronic diseases, especially trunk dieback and viral infections, while seasonal epidemics were correctly controlled by regular pesticide treatments. This general situation, plus an exceptionally dry and hot season, complicated the visual inspection of symptoms, which correctly identified phytoplasma-infected vines in 60% of plants, in line with variability of previously reported surveys [22]. Overall, the experimental plot posed a serious challenge for the reflectance-based prediction of actual phytoplasma infection. The spectral properties of the leaf change when the cellular leaf structure and physiology are altered due to the presence of a pathogen (i.e., FD, BN) which causes metabolic and pigment changes resulting in altered leaf spectral properties of healthy and diseased leaves. Based on the similar pathogenic process of FD and BN diseases, it is not expected that reflectance data may differ between the two. Symptoms of phytoplasma infection due to FDp or BNp are indistinguishable and are related to the peculiar physiological alterations consequent to the activity of phytoplasmas' effectors into the plant phloems [45; 46]. Such changes include the accumulation of soluble carbohydrates with feedback inhibition of photosynthesis and of secondary metabolites involved in the shikimic acid and oxidative pentose phosphate pathways, together with induction of plant defense reactions and alteration of plant hormonal balance [47; 48]. Similar to [PERSON] et al. [4] and [PERSON] et al. [11], some evident differences in leaf reflectance between healthy and diseased grapevines were  
Figure 9: Confusion matrix of the best models ((**A**): logistic regression—LR; (**B**): support vector machine—SVM), (**A**) on test samples, (**B**) on the whole dataset with leave-one-out (LOO) cross-validation. identified. However, higher differences were observed in the reflectances in the Hamamatsu spectra compared to the NIRScan spectra. The reflectance of the Hamamatsu sensor showed small differences between healthy and diseased leaves in the early vegetative growth stages while stronger differences were observed in the later stage (third and fourth week). Higher differences in the reflectance in the VIS spectral region are due to a decrease in pigment concentrations of the infected leaves. Hence, infected leaves absorb less light and reflect more to the sensor [49; 50; 51]. The differences in the Hamamatsu spectra of both groups reveal that in the infected leaves pigment concentration started to reduce in early growth stages even though the leaves had not yet displayed discoloration or yellowing, and this indeed is an indication of the infection in the leaf. The best classification accuracies out of five different classifiers (LR, SVMs, XGBoost, RF, and Cubist) to discriminate between asymptomatic healthy and diseased spectra are summarized in Tables 3-5 where the results indicate that the detection of photoplasan-infected leaves is possible with hyperspectral sensors. Higher classification accuracies are achieved with a maximum of 89% with Hamamatsu data in the fourth week, while the NIRScan sensor shows slightly low accuracies with a maximum of 72% (Table 3). The differences in classification accuracies of the two sensors are due to the reflectance differences between healthy and diseased leaves of each sensor as in the case of the Hamamatsu sensor, where the reflectance of healthy and infected leaves show higher differences compared to the NIRScan sensor.