O1) To design and develop protocols for the extensive acquisition of hyperspectral, solar-induced fluorescence (SIF), and thermal infrared (TIR) data—collected from the leaf level to unmanned aerial vehicles (UAVs)—under field and controlled conditions, in order to establish a comprehensive multi-sensor dataset for plant biotic disease detection.
O2) To retrieve SIF data from novel high-resolution spectrometers mounted on low-flying UAVs, as well as from continuous ground-based SIF measurements, with the aim of detecting plant diseases at early developmental stages.
O3) To evaluate physiology-related optical indicators, including SIF, the Photochemical Reflectance Index (PRI), and TIR, for their effectiveness in identifying the early onset of plant diseases.
O4) To identify specific hyperspectral signatures associated with Fusarium Head Blight (FHB) in wheat and Fusarium Wilt in tomato, and to investigate their relationship with plant-associated microbiota composition and diversity.
O5) To determine the most effective approach for quantifying biochemical and biophysical canopy variables using very high spatial resolution UAV imaging spectroscopy, by comparing different spatial resolutions (ranging from sub-centimeter to several decimeters) and retrieval schemes (e.g., turbid medium versus geometric models), supported by machine learning techniques.
O6) To evaluate the economic and environmental benefits of the proposed multi-sensor remote sensing (RS) approach for the early detection of plant diseases.