Will Edge Computing Save Cloud Farming?

Edge computing is utilized to process time-sensitive data, which is at the core of agriculture.

Fremont, CA: Edge computing is a scattered computing paradigm that drives data storage and computation of earlier data sources, indicating that data does not require to travel far. Hence, farmers can make ad hoc decisions, which is valuable in several situations.

For instance, designing and executing a smart spraying technology system for precise herbicide application needs much appropriate real-time information. However, edge computing cannot save cloud farming because these two technologies are different.

All the talk regarding edge computing replacing the cloud is nothing but bland words, at least until 5G covers the globe. Meantime, edge and cloud computing in agriculture are fully different technologies that are not interchangeable.

The main difference between them is that edge computing is utilized to process time-sensitive data, which is at the core of agriculture. In contrast, cloud computing operates best for data that is not transitory.

Local storage and mini data centers will achieve results. Transmitting real-time images and videos to the public cloud is pointless. Simultaneously, harnessing the potential of 5G, edge computing conveys data on the spot, producing critical real-time information about crops and livestock. Hence, edge computing is the best solution for remote locations with no centralized internet connection.

Edge computing farming: It works

Edge computing has already shown its worth in reducing costs and optimizing yields with AI-driven automation. The thing is that edge computing allows enhanced computer vision, which, when entitled by a mix of 5G and IoT, allows farmers to complete ad hoc works automatically and in the most effective way.

Unfortunately, cloud computing farming is not capable of this. For models, analyzing information from drones requires more than skillfully developed software.

Nominal agribots (autonomous tractors and robotic machinery) are another brilliant instance of the prevalence of edge computing in present digital agriculture. Autonomous tractors and robotic machinery can work on autopilot while communicating with nearby sensors to gain data about the enclosing environment and calculate the most effective paths to cover the essential area, considering the type of task, the number of vehicles at present in the field, the size of implements, etc.

Moreover, they can automatically reroute in case an unexpected obstacle appears in their way. Achieving the same task while harnessing the cloud would be harder.

Edge computing provides farmers with many solutions, which might make it easier to implement various technologies like GPS and GIS to automate and improve their day-to-day agricultural operations.