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Upload,
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running the pipeline
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, and storage
Batch Upload
Benchbot operators manually upload batches to “upload” blob container
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Blob container is mounted to VM
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Upload batches include images and metadata from a single location and capture period. Batches are named after location ID and date, for example, NC_2022-03-22.
Benchbot operators will manually upload (by dragging and dropping) batch folder into the “Uploads” blob container using Azure Storage Explorer.
Running the pipeline and storage
Blob containers are mounted to VM.
A cron job is scheduled to start the pipeline every N hours.
To avoid processing the same batch multiple times, new batches are selected using a batch log file.
The pipeline runs and stores data to temporary locations until complete.
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While blob container do not technically use directories or “folders”, this project uses a pseudo-directory structure for organizing the multiple data products that are being created and updated. The temporary storage locations replicate the pseudo-directory structure used in blob containers.
After the pipeline finishes, the processed batch is logged to the batch log file.
Lastly, temporary batch data is moved permanently to the appropriate blob
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containers.
AutoSfM runs in a docker container
All code is written in Python 3.9
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Pipeline
The pipeline includes seven main processes and places data in five blob containers.
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Upload and Preprocessing
Benchbot operators manually upload batches to “upload” blob container. Images are then processed temporary storage locations
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Preprocessing
Raw images are preprocessed using color calibration card.
Upload batches include images and metadata from a single location and capture period. Batches are named after location ID and date, for example, NC_2022-03-22.
Metadata is made up of:
Ground control point locations csv measured b
Specie map (csv)
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Mapping and Detection
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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, measure in pixel. Camera reference information allows us to convert this local center point to location on the ground or in the real world in a geographical potting area location in meters, (1.23m, 4.56m) for example.
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Object detection is performed to identify plant locations and create local bounding box coordinates. More detailed pixel wise segmentation is performed with the bounding box areas.
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Infers global bounding box positions using autoSfM camera reference information.
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WHY?
The object plant detection model alone does not solve all our problems. It only detect plants , not and cannot differentiate between species. Species information is necessary to create accurate and detailed label dataHowever, to create species-level annotation, each bounding box detection needs a species label.
Species mapping: Species level detection for this project (24 species) is unrealistic at this early stage. When a user-defined species map and geospatial data are applied, AutoSfM results can provide specie level information. If we know what row or general geographic area these species are located, then we can label each bounding box appropriately.
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Segment Vegetation and Cutout Data
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Developed images are passed through the segmentation pipeline that separates vegetation from background. Vegetation is segmented using a combination of digital image processing techniques including index thresholding, unsupervised classification, and morphological operations.
Process Bounding Box Area
Crop image to bbox
Multiply VI by some factor X
Perform unsupervised classification
Apply combination of morphological opening and closing operations
Resulting cutout
Separate Components
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