Thesis subject

UAV-based Disease Detection in Grapevines Combining Multispectral Imagery and Artificial Intelligence

UAVs are a rapid and efficient data gathering system when combined with Geographical information Systems (GIS) and Artificial Intelligence (AI). In agriculture this is very beneficial for farmers because it enables them to assess large crop fields in short time and support farmers in the decision-making.

Botrytis cinereal, a necrotrophic fungus that affects many plant species, most notably wine grapes. This disease could be detected in NVDI maps generated from multispectral images collected by a UAV.


The aim of this project is to develop a UAV data acquisition, processing and analysis workflow for Botrytis cinereal disease detection (BCDD) in vineyards/grapevines. This will be achieved within the main steps:

  1. Review of the state-of-the art in this topic
  2. Design an automatic UAV image workflow for BCDD
  3. Test the approach developed in the available datasets
  4. Writing report


    • Sassu A, Gambella F, Ghiani L, Mercenaro L, Caria M, Pazzona AL. Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture. Sensors. 2021; 21(3):956
    • Di Gennaro, et al. (2016). Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex. Phytopathologia Mediterranea, 55(2), 262-275
    • Pañitrur-De la Fuente, C., et al. (2020). Vigor thresholded NDVI is a key early risk indicator of Botrytis bunch rot in vineyards. OENO One, 54(2), 279–297

      Requirements (optional)

      • Drones/UAV enthusiast
      • Willing to learn more about disease detection with AI
      • Excited to work in topics with high social impact

        Theme(s): Sensing & measuring