Precision irrigation
Plant- and soil-based sensing, crop water status, irrigation scheduling, and variable-rate management for specialty crops.
Explore theme →University of California, Davis
Advancing precision agriculture through sensing, water science, and AI.
I am an Assistant Project Scientist in the Department of Land, Air and Water Resources at UC Davis. My research connects field-scale measurements, remote sensing, hydrology, and machine learning to improve irrigation efficiency, crop productivity, and sustainable resource management.
Srinivasa Rao Peddinti, Ph.D.Assistant Project ScientistLand, Air & Water Resources • UC Davis
25+Research outputs
—OpenAlex citations
—h-index
10+Years in research
Research focus
Technology that connects the field to better decisions.
I work across scales—from soil and plant sensors to aerial and satellite observations—to understand crop water use and translate measurements into practical irrigation intelligence.
Environmental sensing
Eddy covariance, sap flow, dendrometers, soil moisture and water potential sensors, and IoT-enabled monitoring.
Explore theme →Remote sensing & AI
Satellite and aerial imagery, spatial modeling, machine learning, and decision-support systems for agriculture.
Explore theme →Agrohydrology & geophysics
Electrical resistivity tomography, electromagnetic sensing, soil-water processes, and root-zone characterization.
Explore theme →
Research approach
Measure. Model. Map. Manage.
My research integrates high-frequency field observations with spatial data and process understanding. The objective is not only to generate accurate measurements, but to turn them into tools that can support water, nutrient, and crop-management decisions.
- Field-scale sensing and instrumentation
- Remote sensing and geospatial analytics
- Machine learning and process-based modeling
- Grower-oriented decision-support workflows
Latest work
Recent publications
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In the field
Research built around real agricultural systems.
Projects span perennial and annual cropping systems and combine intensive field experimentation with scalable sensing and modeling approaches.
Collaboration
Interested in research collaboration or applied field studies?
I welcome opportunities that connect precision agriculture, hydrology, sensing, and data science with practical resource-management challenges.