Presentation + Paper
21 May 2018 Ground-truthing of UAV-based remote sensing data of citrus plants
Subodh Bhandari, Amar Raheja, Mohammad R. Chaichi, Robert L. Green, Dat Do, Mehdi Ansari , Frank Pham, Joseph Wolf, Tristan Sherman, Antonio Espinas
Author Affiliations +
Abstract
This paper presents the ground-truthing of remote sensing data of citrus plants collected from unmanned aerial vehicles (UAVs). The main advantage of the UAV-based remote sensing is the reduced cost and immediate availability of high resolution data. This helps detect crop stresses throughout the crop season. Near infrared (NIR) images obtained using remote sensing techniques help determine the crop performances and stresses of a large area in a short amount of time for precision agriculture, which aims to optimize the amount of water, fertilizers, and pesticides using site-specific management of crops. However, to be useful for the real-world applications, the accuracy of remote sensing data must be validated using the proven ground-based methods. UAVs equipped with multispectral sensors were flown over the citrus orchard at Cal Poly Pomona’s Spadra Farm. The multispectral/hyperspectral images are used in the determination of vegetation indices that provide information on the health of the plant. Handheld spectroradiometer, water potential meter, and chlorophyll meter were used to collect ground-truth data. Correlations between the vegetation indices calculated using airborne data and proximal sensor data are shown.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Subodh Bhandari, Amar Raheja, Mohammad R. Chaichi, Robert L. Green, Dat Do, Mehdi Ansari , Frank Pham, Joseph Wolf, Tristan Sherman, and Antonio Espinas "Ground-truthing of UAV-based remote sensing data of citrus plants", Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 1066403 (21 May 2018); https://doi.org/10.1117/12.2303614
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CITATIONS
Cited by 1 scholarly publication.
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KEYWORDS
Sensors

Unmanned aerial vehicles

Remote sensing

Near infrared

Vegetation

Image processing

Reflectivity

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