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BLOB-Based AOMs: A Method for the Extraction of Crop Data from Aerial Images of Cotton

Author

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  • Andrew Young

    (Department of Plant and Soil Science, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA
    Cropping Systems Research Laboratory, Agricultural Research Service, United States Department of Agriculture, 3810 4th Street, Lubbock, TX 79415, USA)

  • James Mahan

    (Cropping Systems Research Laboratory, Agricultural Research Service, United States Department of Agriculture, 3810 4th Street, Lubbock, TX 79415, USA)

  • William Dodge

    (Department of Plant and Soil Science, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA)

  • Paxton Payton

    (Cropping Systems Research Laboratory, Agricultural Research Service, United States Department of Agriculture, 3810 4th Street, Lubbock, TX 79415, USA)

Abstract

The use of aerial imagery in agriculture is increasing. Improvements in unmanned aerial systems (UASs) and the hardware and software used to analyze imagery are presenting new options for agricultural studies. One of the challenges associated with improving crop performance under water deficit conditions is the increased variability in the growth and development inherent in low water settings. The nature of plant growth and development under water deficits makes it difficult to monitor the response to environmental changes. Small field and plot-level experiments are often variable enough that averages of seasonal crop characteristics may be of limited value to the researcher. This variability leads to a desire to resolve fields on finer temporal and spatial scales. While UAS imagery provides an ability to monitor the crop on a useful temporal scale, the spatial scale is still difficult to resolve. In this study, an automated computer software framework was developed to facilitate resolving field and plot-level crop imagery to finer spatial resolutions. The method uses a Binary Large Object (BLOB)-based algorithm to automate the generation of areas of measurement (AOMs) as a tool for crop analysis. The use of the BLOB-based system is demonstrated in the analysis of plots of cotton grown in Lubbock, Texas, during the summer of 2018. The method allowed the creation and analysis of 1133 AOMs from the plots and the extraction of agronomic data that described plant growth and development.

Suggested Citation

  • Andrew Young & James Mahan & William Dodge & Paxton Payton, 2020. "BLOB-Based AOMs: A Method for the Extraction of Crop Data from Aerial Images of Cotton," Agriculture, MDPI, vol. 10(1), pages 1-14, January.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:1:p:19-:d:309134
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    References listed on IDEAS

    as
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    2. Rosenow, D. T. & Quisenberry, J. E. & Wendt, C. W. & Clark, L. E., 1983. "Drought tolerant sorghum and cotton germplasm," Agricultural Water Management, Elsevier, vol. 7(1-3), pages 207-222, September.
    3. Larson, James A. & Mapp, Harry P. & Verhalen, Laval M. & Banks, J. C., 1996. "Adapting a cotton model for decision analyses: a yield-response evaluation," Agricultural Systems, Elsevier, vol. 50(2), pages 145-167.
    4. Wanjura, Donald F. & Upchurch, Dan R. & Mahan, James R. & Burke, John J., 2002. "Cotton yield and applied water relationships under drip irrigation," Agricultural Water Management, Elsevier, vol. 55(3), pages 217-237, June.
    Full references (including those not matched with items on IDEAS)

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