As the use of edge AI grows in the agricultural sector, it becomes challenging to continuously monitor edge fleets in the field. Autonomous machines are most valuable when they can operate independently and only alert humans when necessary.
Machines such as those developed by Burro play a crucial role in moving loads and navigating through vineyards and farms. Their effectiveness lies in their ability to function within predefined boundaries and signal any deviations reliably.
Despite efforts by operators, tracking the movement of every machine or monitoring multiple live video feeds is impractical. It is more efficient to have mechanisms automatically filter inputs and handle tasks at a larger scale than human operators can manage.
A recent collaboration between Akamai and Agri Automation Australia resulted in a system that monitors location data from the Burro Cloud API. This system evaluates the data in relation to geofenced areas and sends notifications when specific conditions are met, such as a robot entering a loading zone or moving close to a public access point.
The logic of this system runs on Akamai Functions, a serverless execution environment that operates on WebAssembly. Each code execution is transient, eliminating the need for extensive server provision. The system retrieves the latest robot position, checks it against geofencing rules, and determines if a notification should be sent.
Akamai Functions leverage a distributed edge platform originally designed for handling web traffic. This platform offers low latency and high availability, making it suitable for agricultural settings. The WebAssembly runtime restricts access to the host environment, ensuring code security.
Akamai Functions are increasingly being adopted in the agricultural sector due to their cost-effectiveness and flexibility. These functions can be easily integrated with other services on the platform, allowing for customization based on specific needs.
By utilizing edge execution, processing events close to data sources becomes more efficient. The system can call cloud APIs for data retrieval, but decision-making processes occur at the edge, reducing the response time for interventions.
Overall, the use of edge computing in agriculture streamlines operations, reduces costs, and improves efficiency. With the right technology and infrastructure in place, farmers and agricultural workers can focus on their core tasks while leveraging automation for enhanced productivity.
(Image source: “Male mechanical engineer with sustainable agricultural robot in field” by This is Engineering image library is licensed under CC BY-NC-ND 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/2.0)
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