|YOUNG, ANDREW - Texas Tech University|
|DODGE, WILLIAM - Texas Tech University|
Submitted to: Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/10/2020
Publication Date: 1/15/2020
Citation: Young, A., Mahan, J.R., Dodge, W.G., Payton, P.R. 2020. Blob-based AOMS: A method for the extraction of crop data from aerial images of cotton. Agriculture. 10(1):19. https://doi.org/10.3390/agriculture10010019.
Interpretive Summary: Crops are grown in large fields that typically contain millions of individual plants. What each plant does over a growing season is potentially important. Observing a crop from the ground is difficult since there are many plants covering a large area. By observing the crop from above it is possible to see the entire crop at a moment in time. Satellites, airplanes and drones all provide the ability to observe a crop from a different perspective that can provide valuable information that cannot be obtained from the ground. In this study we have developed a novel method for monitoring crops from drones that allows the user to monitor the crop at any level from the whole field to single plants. This tool will allow researchers to improve crop production through better understanding of environmental variation on crop performance.
Technical Abstract: The use of aerial imagery in agriculture is increasing. Improvements in unmanned aerial systems and the hardware and software used to analyze imagery are presenting new options for agricultural studies uses of these emerging tools. 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 chaotic 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 are often of limited value to the researcher. This variability leads to a desire to be able 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 crop imagery to finer spatial resolutions. The method uses a BLOB-based algorithm to automate the generation of 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, TX 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.