Build solutions that enable every square meter of agricultural land to be treated with the appropriate fertilizer, irrigation, and harvest timeline.
Intro
In the face of increased climate variation and extreme weather, industrial agriculture companies are neutralizing risk through crop insurance. The pricing of crop insurance policies can be remotely informed by historical analysis and predictions of crop health, weather events, and water-related geohazards, such as flooding or irrigation contamination.
Overview
Regular imagery with no minimum AOI
Either task a satellite for specific date ranges or use the latest archive imagery, filtered by date, cloud cover, and area of your customers' infrastructure.
Statistical indexes and machine learning
Out of the box pixel analytics and machine-learning algorithms compatible with satellite imagery optimize agriculture processes.
Processing power that scales
Whether you are running workflows for a single farm or a large industrial agriculture complex, our infrastructure will manage the load.
The technical details
Data and imagery
Varied, industry-leading data sets
Accurately analyzing the health and requirements of crops relies on accurate and holistic input data. Choose from a variety of high-resolution satellite imagery, alongside aerial imagery, weather data, and more. Add the data directly or process it within an UP42 analytics workflow.
Pixel analytics
Apply band math blocks to your imagery
UP42 has a wide array of statistical indices that can provide detailed information about your crops' health. Choose from NDVI, CIgreen, ARVI, EVI, and SAVI to ensure you have the most suitable measure of crop health for your customers' distinct requirements.
Machine learning
Deliver accurate recommendations
Process your high-resolution satellite data with machine-learning algorithms, such as Vultus's Fertilization Zoning Maps. This algorithm from Vultus, as an example, provides your end-users with targeted recommendations on which areas of their agricultural land require higher or lower amounts of fertilizer.
Insight delivery
Automate, embed and scale your analytics
Through the UP42, and supported by the Python SDK, it is easier than ever to integrate the outputs of UP42 workflows into your end-users' products. Trigger your analysis through the Python SDK and access your outputs via download or integrated into your products or cloud storage. For larger AOIs, you can seamlessly scale your analysis by parallelizing your jobs.
import up42
up42.authenticate(project_id="12345", project_api_key="12345")
project=up42.initialize_project()
workflow = project.create_workflow(name="Precision Ag", use_existing=True)
input_tasks=['sentinelhub-s2-aoiclipped','fertilization-zoning-map']
workflow.add_workflow_tasks(input_tasks=input_tasks)
parameter = workflow.construct_parameters(
geometry=[13.41686, 52.51167, 13.422503, 52.514706],
geometry_operation="intersects",
start_date="2020-01-01",
end_date="2020-10-14",)
parameter["fertilization-zoning-map:1"].update({"intersects": {
"type": "Polygon",
"coordinates": [[13.41686, 52.51167], [13.41686, 52.514706], [13.422503, 52.514706], [13.422503, 52.51167], [13.41686, 52.51167]]
}})
job = workflow.run_job(input_parameters=parameter, track_status=True)
job.download_results()