Trial data was collected and used to develop the annotation pipeline and test camera settings. The trial was performed between early-March and late-April (2022) in NC under indoor conditions. Details about the data and results follow.

Setup

• 7 batches of images

• 1 batch = 18-48 images

• sunflower and cereal rye(?) at early growth stages

Annotation Data Pipeline

The WIR is made up of five blob containers that store incoming data.

Upload and Preprocessing

Upload batches include images and metadata from a single location and capture period

Batch name example: NC_2022-03-22

Metadata is made up of:

1. Ground control point locations csv measured b

2. Specie map (csv)

Mapping and Detection

AutoSfM

The AutoSfM process takes in developed images and ground control point metadata to create a global CRS. An orthomosaic, or collage of stitched images, and detailed camera reference information is generated, the latter being used to convert local image coordinates into global potting area locations.

For example, an image 2000 pixels high and 4000 pixels wide has a local center point at (1000, 2000), half its height and half its width. Camera reference information allows us to convert this local center point to location on the ground or in the real world in meters.

Detection

Object detection is performed to identify plant locations and create local bounding box coordinates.

Remap

Infers global bounding box positions using autoSfM camera reference information.

Results