In the run-up to the fair there was a lot of talk about Big Data, artificial intelligence, cloud computing and sensor technology in the farming industry, are farmers soon going to need a degree in computer science to be able to bring in the harvest?
If we look at the innovations presented at Agritechnica over the past ten years, the degree of innovation has indeed shifted increasingly towards electronics, information technology and sensor systems. Seeding, harvesting and feeding machines are being digitally upgraded.
They are networked and becoming increasingly automated. In principle, agricultural technology is playing a pioneering role for other production industries. For example, today, industrial applications are often networked, but then machines in production plants do not usually move around. In agriculture, on the other hand, the machines are mobile, even under difficult outdoor conditions. In the automotive industry, “autonomous driving” is a major trend, but in agriculture, autonomous working is added on.
Are we witnessing the emergence of agriculture 4.0, a digitally upgraded agricultural sector with smart, networked infrastructure?
Farming 4.0 already exists, and we are even one step ahead of Industry 4.0. In principle, networked, and above all mobile production plants on wheels are being used in agriculture. It's just that a factory like this is called a combine harvester, for example. The challenge is to make complex work processes more economical and ecological through a digital transformation process. Mobile work processes in the fields run subject to many disturbance variables: rain, storms, snow, uneven, muddy or bone-dry ground. These are huge challenges to the robustness of the technologies used.
What is the significance of this discipline for agriculture? Where sensors are being used?
In actual fact, everywhere where machines are being made smarter or more efficient for use in an agricultural sector that is increasingly tending towards sustainability. The aim is to use different sensor systems to get an in-depth understanding of every major agricultural issue. In the future, it could mean no sensors, no harvest.
The field is no being longer regarded and treated as a unit. Imaging optical sensor systems – such as laser scanners, stereo cameras and hyperspectral systems – or radar sensors produce important raw data that can be interpreted in relation to soil properties or plant characteristics. In this way, a field can be broken down into the quality of the individual plants: this already the object of research. The sensor data is merged with other data, such as soil or weather data. There are already practical solutions for accessing the various data sources, such as the universal, manufacturer-neutral data-sharing platform for farmers, “agrirouter.“ The main challenges are interpreting the merged data and the resulting action instructions.
The tractor is, in the end, an auxiliary vehicle that mainly pulls other machines. Will it be retired if autonomous field robots take over?
A tractor alone hardly helps anyone – except for the company that sells it, and perhaps the farmer, who can impress his neighbours with a large machine. Apart from that, the tractor is only actually worth anything when linked, mechanically and digitally, to an implement for agricultural processes. The implement is an important part of the unit because it determines the process.
Whether I am fertilizing, collecting hay with a loader wagon or working the soil, the tractor is a tool for a machine that does the actual work. This approach is called Tractor Implement Management (TIM). This means that smart implements for sowing, fertilizing or harvesting are networked with the tractor so that they become one unit. If in the future, implements are to operate on their own as autonomous machines, i.e. the tractor function is integrated into the self-driving implement, then the good old tractor would actually be superfluous.
How do self-driving machines make agriculture safer?
In a huge field in the North American breadbasket, there is, of course, no significant public traffic: it's all about efficiency. Nevertheless, the danger of accidents decreases the fewer people handle complex machines. And during other tasks, such as potato harvesting, there are lots of employees close to a harvesting machine. For example, as part of the “Agro-Safety” research project, we are testing the safety of autonomous feeding machines on farms. This is centred on sensors detecting people and switching the machine off immediately when there is any danger. The development of autonomous systems is – as in the automotive sector – being accompanied by the introduction of hybrid systems, for example in the form of ADAS for autonomous driving and working. This so-called adaptive autonomy also includes autonomous and non-autonomous agricultural machinery working together on a field.
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