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.
Highlights
Agricultural fields are an orderly collection of plants that promote efficient and equal access to resources
The use of the Binary Large Object (BLOB)-based system is demonstrated in the analysis of plots of cotton grown in Lubbock, Texas, during the summer of 2018
The yield per plant (g) as a function of the number of plants in a BLOB varied across the BLOBs, with a mean of 140 g/plant, a minimum of 35 g/plant in BLOB 10, and a maximum of 340 g/plant in BLOB 4 (Figure 7E). These results demonstrate the data that can be extracted from the BLOB-based areas of measurement (AOMs) using this approach
Summary
Agricultural fields are an orderly collection of plants that promote efficient and equal access to resources. Efficient crop management is based upon meeting the resource demands of a crop in the proper amounts at the proper times. Variation from the desired populations and distribution of plants within a field can reduce yield as compared to a more uniform planting [1,2,3,4]. Understanding the resource demands of the crop (radiation, water, and nutrition), and planting in a manner that provides for those needs, is an essential part of modern agriculture. Replanting to achieve uniformity is an option but producers are often uncertain about the value of replanting, since, if the replant does not increase yield, it is seen as waste of time and resources. Data-based replant decisions require information on the extent of stand variability and the potential effect of that variability on yield, perhaps one hundred days later
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