Keywords: plant phenotyping, UAS, image analytics, gridding, open-source software, Python.
INTRODUCTION
Plant phenotyping is a vital practice in plant breeding and biotechnology to discover a new variety or characterize desired genetic traits resistant to biotic and abiotic stresses. High throughput phenotyping (HTP) is essential to meet the timely delivery of the phenotypic metrics of a large number of plots. Conventional phenotyping is made by notetaking to score the plant health conditions, which is laborious and inconsistent. Image-based HTP was deployed to replace manual scoring and achieve the rapid extraction of phenotypic metrics using ground and aerial platforms. Plot-level metrics is required for plant phenotyping on a large number of plots and extracted by defining a region of interest (ROI) of the field boundary and processing sub-ROIs aligned with rows and columns of the total number of plots, called gridding. Grid-based image processing is offered by commercial software (e.g., ArcGIS) but is limited to upright rectangular fields and manual drawing of polygons.
Unmanned aircraft system (UAS) has been widely used to deliver a massive volume of images in high resolution and extract metrics by examining spectral signature and morphological features of the plants. Raw UAS images are preprocessed for orthomosaicing through global positioning system (GPS) and inertial measurement unit (IMU) information using commercial software (e.g., Pix4Dmapper, Agisoft). The orthomosaiced image is georeferenced to align the image top to north, whereas the field orientation is often off the north, resulted by various field layout and declination of magnetic north (used by IMU) from geographic north up to 20 degrees [1]. In practice, no fields are truly oriented to north. This misalignment of the field orientations occurs to any airborne images not only from UASs but also from manned airplanes or satellites, as their image products are all georeferenced. Due to the misaligned orientation, gridding requires a preprocess of image rotation to make the field orientation upright to be aligned with the grid [2], because the computation pattern is sequenced by row (i) and column (j) in image coordinates and performs metrics extraction based on upright rectangular ROIs. Finding a rotation degree, however, takes multiple adjustments to precisely align sub-ROIs with plots across the field, which is a laborious time-consuming task and leads to a heavy computational load especially on the big-sized (e.g., >1 GB) orthomosaic image. Image rotation also creates resampling errors due to the changes of pixel values in repositioned geometry. To solve this issue, gridding method must be generalized for the various field orientations and sizes without changing the original image. The grid rotation and metrics extraction on the rotated sub-ROIs are key challenges in image processing.
This adaptive gridding method will help creating a GIS interface of the grid by converting sub-ROIs to a shapefile that contains a list of plot polygons with GPS coordinates.
The objective of the study is to develop open-source software that provides a quick extraction of plot-level metrics of the field image without the image rotation. Specific objectives are to 1) develop algorithm to create a rotated grid that fits the field and plot boundaries aligned with all sub-ROIs, 2) to extract metrics on the rotated ROIs by geofencing algorithm, 3) publish the software available to the public that automates the adaptive gridding process in graphic user interface (GUI).
MATERIALS AND METHODS
UAS images are collected for field mapping and registered with GPS coordinates and IMU data based on flight waypoints and signal communication (Fig. 1). The UAS receives GPS signals from satellites and correction signals from a real-time kinematic (RTK) base station or NTRIP (Networked Transport of RTCM via Internet Protocol) service. The raw UAS images are preprocessed for stitching tile images to an orthomosaic image in field level.