Teacher: Ludovico Biagi
Tutor: Nikolina Zallemi
Description: This project aims to develop a complete UAV-based crop health monitoring system integrating geospatial processing, temporal analysis, and WebGIS visualization. Multispectral and thermal drone surveys are periodically acquired over agricultural fields and processed in Pix4D to generate orthomosaics, reflectance products, and thermal maps. Python-based workflows are then used to compute vegetation indices such as NDVI, NDRE, and GNDVI in order to evaluate crop conditions and detect possible stress areas. The processed raster datasets are organized as temporal series and published through GeoServer using temporal raster management solutions. The final output of the project is an interactive WebGIS platform where users can visualize, compare, and explore drone acquisition results over time. The platform is designed to provide an intuitive interface for monitoring agricultural fields and supporting precision farming applications. An additional future extension could include the integration of satellite data through Google Earth Engine. Furthermore, the project could be conceptually presented as the prototype of a potential startup service, where farmers may use UAV technologies and online geospatial platforms to continuously monitor the health and evolution of their crops.