Location: Soil and Water Management ResearchTitle: Wireless canopy sensing network systems for automated control of irrigation and water use efficiency Author
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/16/2013
Publication Date: 9/8/2013
Citation: Oshaughnessy, S.A., Evett, S.R., Colaizzi, P.D. 2013. Wireless canopy sensing network systems for automated control of irrigation and water use efficiency[abstract] Automated methods for continuous measurements agro climatalogy and forestry workshop, Ben Gurion University, Israel, September 7-12, 2013.p. 7. Interpretive Summary:
Technical Abstract: Ground-based instrumentation for plant canopy sensing (infrared thermometry and spectral reflectance sensors) has been used extensively in agriculture to monitor crop status. Typically, measurements are accomplished with handheld or vehicle mounted instrumentation during limited periods of a day, and a growing season. However, with the arrival of wireless sensor network systems, it is possible to accomplish continuous crop canopy monitoring over daylight hours throughout a growing season. Coupling wireless plant canopy sensing network systems with mechanical move variable rate irrigation (VRI) systems allows for supervisory control and data acquisition (SCADA) to accomplish site-specific irrigation control over a large-size field. This presentation will provide an overview of fundamental components of our SCADA system for crop irrigation management, and the benefits of continuous spatial and temporal crop monitoring. Essential components include pertinent radio frequency communication protocols, network architecture, wireless sensor nodes, moving mechanical variable rate irrigation systems, and algorithms for temperature scaling and irrigation scheduling. Direct benefits from wireless canopy sensing SCADA systems include automated control of crop water use efficiency, a tool for spatial and temporal monitoring of abiotic and biotic crop stress, dynamic prescription map building to address within-field temporal variability, and a method to predict seasonal crop yield.