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The application of small unmanned aerial systems for precision agriculture: a review

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Abstract

Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.

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Acknowledgments

This research was supported by a Grant (project #920161) provided to John M. Kovacs from the Northern Ontario Heritage Fund Corporation of Canada.

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Authors and Affiliations

  1. Department of Geography and Geology, Algoma University, 1520 Queen street East, Sault Ste. Marie, ON, P6A 2G4, Canada

    Chunhua Zhang

  2. Department of Geography, Nipissing University, North Bay, ON, P1B 8L7, Canada

    John M. Kovacs

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  1. Chunhua Zhang
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  2. John M. Kovacs
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Correspondence to Chunhua Zhang.

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Zhang, C., Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric 13, 693–712 (2012). https://doi.org/10.1007/s11119-012-9274-5

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