Abstract
Aerial imagery has the potential to advance high-throughput phenotyping for agricultural field experiments. This potential is currently limited by the difficulties of identifying pixels of interest (POI) and performing plot segmentation due to the required intensive manual operations. We developed a Python package, GRID (GReenfield Image Decoder), to overcome this limitation. With pixel-wise K-means cluster analysis, users can specify the number of clusters and choose the clusters representing POI. The plot grid patterns are automatically recognized by the POI distribution. The local optima of POI are initialized as the plot centers, which can also be manually modified for deletion, addition, or relocation. The segmentation of POI around the plot centers is initialized by automated, intelligent agents to define plot boundaries. A plot intelligent agent negotiates with neighboring agents based on plot size and POI distributions. The negotiation can be refined by weighting more on either plot size or POI density. All adjustments are operated in a graphical user interface with real-time previews of outcomes so that users can refine segmentation results based on their knowledge of the fields. The final results are saved in text and image files. The text files include plot rows and columns, plot size, and total plot POI. The image files include displays of clusters, POI, and segmented plots. With GRID, users are completely liberated from the labor-intensive task of manually drawing plot lines or polygons. The supervised automation with GRID is expected to enhance the efficiency of agricultural field experiments.
Highlights
Agricultural field experiments have an advantage over greenhouse experiments because environmental conditions in the field are closer to real-world situations
The field layout is in a straight grid, but some of the plots have connected leaf canopies, which usually poses a challenge for existing segmentation methods
GReenfield Image Decoder (GRID) is capable of detecting different types of field layouts, including plots arranged in grid or rhombus patterns
Summary
Agricultural field experiments have an advantage over greenhouse experiments because environmental conditions in the field are closer to real-world situations. To record and utilize such characteristics from field experiments, orthoimages can serve as the digital media for transferring the information. This type of image is acquired from satellites or unmanned aerial vehicles (UAV), having been adjusted for lens distortion and camera tilt. Orthoimages are saved in Geographic Tagged Image File Format (GeoTIFF) This file format can record more than three imagery channels, allowing scientists to explore information beyond visible wavelengths, such as near-infrared (NIR). GeoTIFF can embed geographical information into orthoimages To use these images for field experiments, plot boundaries must be defined for segmentation, and the pixels of interest (POI) must be extracted. Several roadblocks prevent the use of orthoimages for high-throughput phenotyping for agricultural experiments
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