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

Andrew Young, James Mahan, William Dodge and Paxton Payton
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Andrew Young: Department of Plant and Soil Science, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, 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

Agriculture, 2020, vol. 10, issue 1, 1-14

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.

Keywords: UAS; Aerial Imagery; BLOB; cotton; water deficit; variability (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
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