Dr. Ittai Herrmann
Remote sensing of vegetation, Hyperspectral imaging, Precision agriculture, High-throughput plant phenotyping
(https://orcid.org/0000-0003-1136-1883)
The PSL research motivation is to secure the ever-growing human population necessities by increasing agricultural productivity while enhancing resource use efficiency and sustainably. Our interest is at identifying relations between remotely sensed spectral, spatial and temporal data and morpho-physiological plant traits. The dynamic environmental conditions and genetic diversity as well as their combination affect the plant physiology, morphology, function and productivity. Thus, plant traits assessment can be improved by fusing remotely sensed data and products, environmental information and genetic data to a multimodal data set analyzed by machine learning techniques. Such algorithms will improve breeding program efficiency, which will ultimately enhance productivity and stress tolerance as well as increase resource use efficiency in commercial fields.