Agro-informatics and Remote Sensing Laboratory research projects focusing on plant and soil interactions to study environmental and climate change processes using remote sensing and data sciences. Studying such complex ‎interactions of multivariate and ‎multi-scaled ‎problems requires quantitative and ‎interdisciplinary approaches. Consequently, ‎our research ‎increasingly involves developing and using computer models, ‎ state-of-the-art ‎‎remote sensing ‎technologies, earth observation systems, ‎spectroscopy, ‎spatiotemporal ‎‎modeling, big-data analytics, ‎and machine learning modeling‎ with advanced imaging ‎‎processing ‎methods. Our research focuses on the following topics:
1. Soil and plant sensing - monitoring plant and soil conditions to assess biotic and abiotic stress by integrating multi-sensor platforms. We detect plants’ phenology status and soil health based on remote sensing.
2. Precision agriculture - assessment of agricultural systems response to environmental variability.
3. Forest response and agriculture to environmental change- assessment of forest systems response to environmental and humane-derived factors, from the tree to the landscape scale.
4. Data processing and modeling- includes applying and developing artificial intelligence, machine ‎learning, machine vision and ‎optimization algorithms, spatial data analysis, and ‎time series analysis.‎
5. Remote sensing (sensors, algorithms, applications) - ‎imaging spectroscopy, light detection and ranging (LiDAR), hyperspectral, multispectral, ‎‎thermal imaging, UAV application, plant and soil spectroscopy, and satellite data analysis.