Cloud Agronomics uses remote-sensing technology and AI to provide growers with insights into their crops and soil, leading a new wave of proactive analytics to lower greenhouse gas emissions and spur sustainable food production.Learn about Cloud Agronomics
There is a data gap in agriculture that prevents the agribusiness industry from making informed decisions, forcing them to farm reactively instead of proactively. Current methods of taking physical samples to a lab or using satellite imagery are expensive and inefficient, resulting in higher greenhouse gas emissions that contribute to climate change. Growers need a verifiable system to analyse crop and soil nutrients, predict yields and monitor carbon sequestration in soil at scale.
Cloud Agronomics scans crops and soil using custom hyperspectral imaging apparatuses on manned aircraft, collecting 300 times more data per pixel than satellites. The data is sent to Azure, where georeferencing, calibration and analysis algorithms convert the raw data into insights. Cloud Agronomics is building one of the largest tagged datasets for agriculture, enabling growers to manage crops proactively and efficiently. In addition, real-time carbon monitoring will give carbon credit marketplaces the first standardised carbon index to provide a financial incentive to transition to carbon farming, which is the practice of removing excess carbon from the atmosphere and storing it in soil to aid plant growth.
Creating a living map of global agriculture
Farmland has the capacity to sequester massive amounts of atmospheric carbon by adopting regenerative practices. Cloud Agronomics is tracking soil carbon content as well as analysing crop deficiencies caused by disease or soil deficiencies. This data can be scaled to build a living map – farm by farm, state by state and country by country.
How Cloud Agronomics works
Growers want to use their land more efficiently. Aircraft with hyperspectral imaging collect data on soil, disease activity and crop performance. Local and satellite data are used to create a global geospatial dataset. AI extracts insights and real-time carbon monitoring. The grower applies insights to produce food more sustainably. Carbon credit marketplaces can use the index to incentivise carbon farming.
How Cloud Agronomics uses Azure
- Blob Storage to store calibrated hyperspectral and satellite data for geospatial querying
- Databricks for pre-processing and orchestrating machine learning
- Container Registry to provide the storage and registration for containerised applications
- Container Instances to run containerised applications, including ML models, visualisation and user-facing apps
- Machine Learning Service for real-time analytics for data science R&D
- Kubernetes Service for orchestration and cluster management for processing calibrated hyperspectral imagery