Towards collaboration between unmanned aerial and ground vehicles for precision agriculture

Subodh Bhandari; Amar Raheja; Robert L. Green; Dat Do

doi:10.1117/12.2262049

8 May 2017 Towards collaboration between unmanned aerial and ground vehicles for precision agriculture

Subodh Bhandari, Amar Raheja, Robert L. Green, Dat Do

Author Affiliations +

Proceedings Volume 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II; 1021806 (2017) https://doi.org/10.1117/12.2262049
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

This paper presents the work being conducted at Cal Poly Pomona on the collaboration between unmanned aerial and ground vehicles for precision agriculture. The unmanned aerial vehicles (UAVs), equipped with multispectral/hyperspectral cameras and RGB cameras, take images of the crops while flying autonomously. The images are post processed or can be processed onboard. The processed images are used in the detection of unhealthy plants. Aerial data can be used by the UAVs and unmanned ground vehicles (UGVs) for various purposes including care of crops, harvest estimation, etc. The images can also be useful for optimized harvesting by isolating low yielding plants. These vehicles can be operated autonomously with limited or no human intervention, thereby reducing cost and limiting human exposure to agricultural chemicals. The paper discuss the autonomous UAV and UGV platforms used for the research, sensor integration, and experimental testing. Methods for ground truthing the results obtained from the UAVs will be used. The paper will also discuss equipping the UGV with a robotic arm for removing the unhealthy plants and/or weeds.

Citation Download Citation

Subodh Bhandari, Amar Raheja, Robert L. Green, and Dat Do "Towards collaboration between unmanned aerial and ground vehicles for precision agriculture", Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 1021806 (8 May 2017); https://doi.org/10.1117/12.2262049

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