Use of optical sensors to early detect Flavescence Dorée symptoms on Vitis vinifera plants

Introduction: Vitis vinifera, one of the most cultivated crops in Italy, can be affected by a number of diseases, among which the Flavescence Dorée (FD). FD is caused by Phytoplasmas, wall-less bacteria belonging to the order Mollicutes, and it is trasmitted by the vector insect Scaphoideus titanus. FD is widely present in the vineyards of the North of Italy, in particular in Piedmont. Despite National Government efforts in order to limit the spread of this disease, every year many cases of FD suffering vines are recorded. Hence, to find innovative solutions with low environmental impact to counter the spread of FD is an important topic. The aim of this work is to verify the possibility to detect in advance the FD symptoms using optical sensors, investigating if a correlation between FD and a specific reflectance occurs, in order to focus the application of biological elicitor of resistance against pjytoplasmosis. Map of vineryard Plant affected by FD Portable G.P.S. Optical sensor "mobile lab” Working principles of OptRX. Materials and methods: Three different vineyards were selected according to the precence of cv. Dolcetto, their position and the occurence of FD. The vineyards were handly mapped and then a relief to assess single FD and Esca disease symptoms was carried out. Then the diseased plants were georeferenced with a portable DGNSS. Finally a relief with the optical sensor OptRX was made in the three vineyards. The OptRx sensor measures the reflectance at VIS (670 nm), RedEdge (730 nm) and in the range of NIR (780 nm) and also calculates two different indices: NDRE and NDVI. For each row, the mobile mean was calculated for NDRE and NDVI indices. Then the difference between each point of relief and the mobile mean was compared to the mean of the index inside the row. Values higher than 10% and 20% were considered of attention or high attention, respectively. All the values of reflectance at VIS, RedEdge, NIR and of NDRE and NDVI indices were used to create thematic maps with QGIS software. Here we show only NDRE maps with the position of diseased plants and levels of attention (10%) and high attention (20%). To check the efficacy of the method used in this work, we compared the indication given by the maps with the real situation of the diseased plants, and identified true positives or negatives and false positives or negatives to calculate: sensitivity, specificity, Positive Predicive Value (PPV) and Negative Predictive Value (NPV). Conclusions and perspectives: The method used in this work showed high sensitivity and specificity values combined with a very high Negative Predictive Value (about 99%). However a high number of False Positives were recorded and consenquently we obtained a low Positive Predictive Value. For this reason during this season we will carry out a new relief using a more complex “mobile lab” with six OptRX sensors combined to two LIDAR sensors.