How can I use Machine Learning in Agriculture?
Machine Learning is quickly becoming a part of our every day lives. Many people don't realize just how powerful this tool is. Here are a couple of examples where we can help automate monotonous, resource intensive, or even dangerous tasks in Agriculture.
Center Pivots are among the most high-tech machines in agriculture today. They can stretch more than 5 football fields in length, and are responsible for irrigating much of western Nebraska. There are numerous sensor suites available, but Bloc Vision is looking to give you critical performance data with one, non-contact system. By strategically placing cameras at each span, you can gauge sprinkler output, drive strength, and coupling orientation. The system learns the pivot characteristics over time, and alerts you to any sudden changes in performance.
Calving is a time-critical part of any cattle operation. The farmer must be on call to deliver assistance in the event of any difficulty during or after birth. Cameras are becoming an integral part of the process, allowing the farmer to periodically monitor their cattle remotely. What if a computer could do this work for you? By learning behavior patterns of the cow, Machine Learning can detect agitation, tail movement, and repeat sitting that might indicate the time is near. Alerting you right away to any health issues.
Weed & Pest Control
Leafy Spurge is a major problem in Nebraska agriculture. This bright yellow plant reduces pasture productivity, and interrupts native grasslands. Regular monitoring is important in the control of this invasive species. Advances in drone technology mean it's now possible to view large swaths of land in a single flight, but how do you process this video feed automatically? The Bloc Vision platform is geared for deployment on low power, remote systems. Its video processing technology can identify invasive species mid-flight, leaving you with a more accurate map for herbicide application.
Bulk Grain Loading is the perfect application for Machine Learning in agriculture. This two person task relies on a spotter to observe the material exiting the conveyor and to guide the hopper back and forth as it fills. Algorithms running on a Bloc Vision system can gauge capacity, flow rate, and gate control to automate loading. Truck movement can be more accurately instructed using predictive models for grain behavior as it leaves the conveyor. This reduces wait times, and increases worker productivity with a single device.