Soil moisture sensors based estimation of hybrid bermudagrass quality using remote sensing technology (Conference Presentation)

Priti Saxena; Subodh Bhandari

doi:10.1117/12.2557921

27 April 2020 Soil moisture sensors based estimation of hybrid bermudagrass quality using remote sensing technology (Conference Presentation)

Priti Saxena, Subodh Bhandari

Author Affiliations +

Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; 114140D (2020) https://doi.org/10.1117/12.2557921
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

In recent years, applied irrigation has been reduced to comply with California’s mandated water use restrictions. In an effort to increase water conservation, employing new technologies such as soil moisture sensors (SMS) in the agriculture system is imperative. The overall goal of this project is to estimate bermudagrass quality, managed under SMS based irrigation scheduling at Cal Poly Pomona’s Center for Turf, Irrigation and Landscape Technology (CTILT). UAV mounted Hyperspectral sensor and hand-held spectroradiometer are being used to determine vegetation indices such as water band index (WBI) and normalized difference vegetation index (NDVI) and are compared with water and chlorophyll content of bermudagrass. The UAV platform used is a multicopter, which is equipped with GPS and autopilots for autonomous flight and data capture over the turfgrass plots. The visual turf quality ratings, remote and proximal sensor data are collected once every two weeks during the growing season. The handheld spectroradiometer is a hyperspectral devise and is used to validate UAV mounted hyperspectral sensor data. A general linear model analysis of variance for a randomized complete block design will be conducted for each date to test SMS based irrigation effect on the visual ratings and clipping yield. Comparisons among visual quality ratings, percentage green cover, NDVI and WBI are analyzed with the general linear model of correlation (Pearson’s). Differences between means were separated by Fisher’s protected least significant difference (p = 0.05).

Conference Presentation

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Priti Saxena and Subodh Bhandari "Soil moisture sensors based estimation of hybrid bermudagrass quality using remote sensing technology (Conference Presentation)", Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 114140D (27 April 2020); https://doi.org/10.1117/12.2557921

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KEYWORDS

Sensors

Remote sensing

Soil science

Sensor technology

Unmanned aerial vehicles

Visualization

Vegetation

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