Uavine: UAV Hyperspectral and RGB Image Dataset of a Vineyard - Temporal Canopy Growth Monitoring Using Leaf Area Index

Remote sensing methods employing Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral sensors, facilitate data acquisition, allowing for a precise analysis of plant health and growth. These methods are characterized by their speed and non-destructiveness. Hyperspectral imagery has a great potential for estimating the Leaf Area Index (LAI), which is crucial for measuring vegetation density. In this work we introduce a novel multi-purpose dataset from a vineyard field in the Korinthos region, Southern Greece, where UAV images were acquired from a Specim AFX-10 camera, with 224 spectral bands as well as from a RGB camera (Phantom 4 Pro DJI); the images were georeferenced by using Ground Control Points (GCP). An experimental analysis of the hyperspectral dataset was conducted to examine the canopy growth rate on three different days, aiming to distinguish the differences between fertilized and non-fertilized vines, with the dataset being available at https://github.com/aelsaer/uavine