We live in a moment of strong technological advance in agriculture, in which the high speed of implementation of innovations have generated increases in productivity and efficiency of the sector even in a period of global crisis. Agriculture 4.0 is increasingly close to reality, and this is largely due to the development and expansion of technologies such as Artificial Intelligence, Big Data and Internet of Things (IoT). In terms of processing, the gain in scale in the application of these technologies was strongly supported by Cloud Computing, a 2000s movement of centralisation of computing capacity by major global representatives.
However, in a scenario of growing technological demand, the need to improve performance in information processing has made it essential to create a tool capable of filling some gaps left by cloud solutions. And that's where Edge Computing comes in, to complement the well-known cloud.
The trend is growing worldwide. According to recognised global consulting firm Gartner, by 2022, 75% of corporate data will be generated and processed by Edge Computing environments. In agribusiness is no different, and the application is already generating important results.
Processing in agriculture
Edge Computing is like a specialisation of the Internet of Things. Without it, all data collected through IoT devices are sent to a cloud centre for processing. With the new technology, on the other hand, the collected data is classified locally, so that part of it is processed right there, on the "edge" of the network — hence the name edge computing — through micro data centres.
Thus, only certain information is sent to a cloud centre, while those that often need to be consulted are analysed on the device itself — in the case of agribusiness, in the field - reducing data traffic.
This ability to perform advanced analytics close to the data source meets the market's need to cope with increasing traffic demands. As there is a screening of the information that will be sent to the processing centre, transfer rates are optimised.
Solution to connectivity and latency challenges
The main benefits of Edge Computing are the reduction in bandwidth required for sending and processing data and the decrease in latency, which is the response time of a request — the period in milliseconds that a data takes to navigate from where it was generated to where it will be processed. Both advantages are possible due to the proximity between the processing location and the origin of the information; only with cloud computing, on the other hand, all data would need to travel long distances before returning to consumption.
In practice, this reduction in latency helps in real-time data access, which is essential for the implementation, with maximum effectiveness, of digital and intelligent solutions in agricultural processes. The HxGN AgrOn Ti10 display, launched by Hexagon this year, for example, already has a next-generation processor that ensures the execution of complex software, being able to process both advanced algorithms and synchronisation between machines. With this differentiated processing, certain functions can be performed on the equipment itself through Edge Computing, making it easier to make smarter and more agile decisions.
Another example is the use of this technology in an agricultural spraying activity, in which sensory devices are enabled to determine alone which area should be sprayed, using the data collected and analysed by the devices themselves.
As Edge Computing also reduces the bandwidth required for processing, the solution becomes even more useful for agricultural applications — considering that, in general, we still have major connectivity problems in the field in Brazil.
In the current context, in which IoT solutions already allow wide integration between various products, such as sensors, on-board computers, edge computing machines, is playing an increasingly important role in the application and evolution of technology in the field. It is one of the technologies that will have increasing adoption in the coming years, accelerating the consolidation of the digital transformation of agribusiness