A semi‐automatic workflow for plot boundary extraction of irregularly sized and spaced field plots from UAV imagery. Issue 1 (5th April 2022) - Ha, Thuan; Duddu, Hema; Vandenberg, Albert; Shirtliffe, Steve
Abstract: Advances in high‐throughput platforms such as UAVs (unoccupied aerial vehicles) facilitate rapid image‐based phenotypic data acquisition. However, existing plot‐level data extraction methods are unreliable if field plots differ in size and spacing, as often occurs in early‐generation plant breeding trials. To overcome the limitations of conventional plot extraction techniques, a combinational approach with both field‐map information and image classification techniques can be used to optimize plot extraction. The objective of this study was to develop a plot boundary extraction workflow for irregularly sized and spaced field plots from UAV imagery using plot spacing data and vegetation index‐based classifiers. An herbicide screening experiment consisting of three replications of 780 lentil ( Lens culinaris  Medik.) populations was foliar sprayed with saflufenacil. Aerial image acquisition was conducted during the peak vegetation stage using a RedEdge multispectral camera. A semi‐automatic workflow was compiled in eCognition software to extract lentil plot boundaries. Normalized difference vegetation index (NDVI) was calculated to locate the plots with vegetation and those with low NDVI or no vegetation, and pixel resizing based on plot size and orientation was used to draw the plot boundary. The extraction results showed a precise estimation of plot boundary for all the plots with a wide range of herbicide damage, including the plots with complete loss of vegetation. By using a simple convolutional filter (line filter), image thresholding, and pixel resizing, this approach avoided the use of complex algorithm‐based methodologies. Results suggest that this workflow can be extended to a wide range of phenotyping studies. Core Ideas: Conventional plot extraction methods are unreliable if field plots differ in size and spacing. A combination of field‐map data and image classification can be used to optimize plot extraction. A simple convolutional filter, image thresholding, and pixel resizing were used in the study. The workflow avoided the use of complex algorithms for plot boundary extraction of uneven plots.