This work suggests an innovative methodology for quantitative and qualitative analysis of spatial distribution of symptomatic plants. Remote sensing and ground observation data were analyzed to promptly identify the early stages of disease, even before visual detection. Each vine was located with a global positioning system, and classified for appearance of foliar symptoms and disease severity at weekly intervals from the beginning of each season.
The dataset consists of images collected from30 flights: 9flights with senseFly. UAV-based images were collected with several sensors over the experimental area, starting from 7:25am and ending at 20:00pm local solar time. High-resolution multispectral images were acquired during June and July of 20, in an experimental vineyard heavily affected by GLSD, located in Tuscany (Italy), where vines had been surveyed and mapped since 2003. University and Research(WUR) farm maintained by Unifarm. This approach showed high correlation between Normalized Differential Vegetation Index (NDVI) acquired by the UAV and GLSD symptoms, and discrimination between symptomatic from asymptomatic plants. Multispec GSD (per multispec band) 5.28 cm per pixel at 120 m. The present study suggests a methodology to investigate the relationships between high-resolution multispectral images (0.05 m/pixel) acquired using an Unmanned Aerial Vehicle (UAV), and GLSD foliar symptoms monitored by ground surveys. We also demonstrate an integration of our system with a novel broadband spectrometer sensor for detection and characteri- zation of gases developed at Heriot-. Altum-PT thermal imagery provides powerful insights into water management, helping irrigation managers determine the correct watering and identify leaks or clogs in the irrigation system by the difference in crop canopy and soil temperature. Foliar symptoms of grapevine leaf stripe disease (GLSD, a disease within the esca complex) are linked to drastic alteration of photosynthetic function and activation of defense responses in affected grapevines several days before the appearance of the first visible symptoms on leaves. Sensors discussed were multiSPEC 4C, MicaSense RedEdge 3, and DJI Phantom4 Multispectral (DJI P4M).
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