Join this session to learn how the CVML and Data Platform team at BlueRiver Technology utilized Databricks to maximize savings on herbicide usage and revolutionize Precision Agriculture.

Blue River Technology is an agricultural technology company that uses computer vision and machine learning (CVML) to revolutionize the way crops are grown and harvested. BRT’s See & Spray technology, which uses CVML to identify and precisely determine whether the plant is a weed or a crop so it can deliver a small, targeted dose of herbicide directly to the plant, while leaving the crop unharmed. By using this approach, Blue River significantly reduces the amount of herbicides used in agriculture by over 70% and has a positive impact on the environment and human health.

The technical challenges we seek to overcome are: 

• Processing massive petabytes of proprietary data at scale and in real time. Equipment in the field can generate up to 40TBs of data per hour per machine.


• Aggregating, curating and visualizing at scale data can often be convoluted, error-prone and complex. 


• Streamlining pipelines runs from weeks to hours to ensure continuous delivery of data. 


• Abstracting and automating  the infra, deployment and data management from each program.


• Building downstream data products based on descriptive analysis, predictive analysis or prescriptive analysis to drive the machine behavior.

The business questions we seek to answer for any machine are: 

• Are we getting the spray savings we anticipated?


• Are we reducing the use of herbicide at the scale we expected?


• Are spraying nozzles performing at the expected rate?


• Finding the relevant data to troubleshoot new edge conditions. 


• Providing a simple interface for data exploration to both technical and non-technical personas to help improve our model.


• Identifying repetitive and new faults in our machines.


• Filtering out data based on certain incidents.


• Identifying anomalies for e.g. sudden drop in spray saving, like frequency of broad spray suddenly is too high.

How we are addressing and plan to address these challenges:

• Designating Databricks as our purposeful DB for all data - using the bronze, silver and gold layer standards.


• Processing new machine logs using a Delta Live table as a source both in batch and incremental manner.


• Democratize access for data scientists, product managers, data engineers who are not proficient with the robotic software stack via notebooks for quick development as well as real time dashboards.