Berlin – Dirk Siemers pulls your smartphone out of your pocket and opens the app.
“Here I have value from the stable,” says the 43-year-old farmer who runs hen laying operations in Daiholtz, a town of less than 50,000 people in Lower Saxony.
In 2019, he took over the business from his father, built a modern stable for free-range farming, and currently owns 12,000 animals.
Siemers can use the app to check temperature data, humidity levels, feed silo fill levels, or barn fresh air requirements. Modern sensors also provide data on water or feed consumption per animal.
“We’re trying to capture as much data as possible here,” explains the farmer who studied computer science and worked as an IT specialist for many years before taking over the business.
Data is key to the use of artificial intelligence (AI) in agriculture, he says.
“Artificial intelligence learns from examples,” says Christian Lamp, an AI expert at Big Dutchman, a supplier of livestock building equipment. He explains that there is a fundamental difference between AI and traditional digital applications.
In traditional applications, individual features are defined. For example, a camera that recognizes chicken has a white area with a red area.
The problem is that when it gets dark or changes the light, everything appears to be reddish, which makes the system no longer recognize white areas.
Systems that use AI work differently. The system is fed data – in this case many different images of chickens.
You need thousands of different images that AI will store as chicken images. Based on this, the system can train itself independently. Therefore, AI-based systems are prone to errors. Furthermore, AI can recognize patterns that humans cannot do.
All AI that is trained is just as good as the data it supplies, says Ramping.
“We need AI that can process as much data as possible, so we need enough data.” The challenge is not only to get the data, but to ensure its quality and preparation.
An AI system requires a huge amount of data to function. Physical quantities can be recorded, explains Jörg Kleine-Klatte, who heads Big Dutchman’s digital business unit.
Not only water and supply consumption or weight, but also the values of ammonia levels or carbon dioxide concentration (CO2) in the barn air. All these factors affect the health and welfare of animals.
Image processing has made great strides over the past five years. Now, AI can use cameras to determine the weight of an animal. But now it is possible to recognize motor patterns, explains Kleine-Klatte.
For example, conclusions can be drawn from such data on animal health. A well-known example of chickens is to get exposed to dust. This includes chickens that roll down through dust and dry soil to remove parasites from their fur, wings or skin.
“This is a clear indication that the animal is feeling better,” says Kleine-Klatte.
“The big advantage of AI is that it allows you to monitor large farms 24/7,” explains Ramping. Until now, farmers have been able to get a snapshot of what’s going on in the barn when they checked the animals on tour.
Only then can they observe the animal’s behavior, determine what the barn’s climate is, or how clean the garbage material is. On the other hand, with AI, you can see what’s going on in the barn every day, allowing you to respond faster.
The first applications of AI are already in use in many areas of agriculture, says Joachim Hertzberg. The computer scientist taught at Osnabrück University until his retirement last summer and was the managing director of the local branch of the German Center for Artificial Intelligence Research (DFKI).
The harvester is equipped with a sensor system that ensures that the grain is threshed correctly.
According to Hertzberg, these sensors take into account factors such as grain size and moisture content to see if the grain is being processed at the appropriate thickness.
Cameras installed on the machine can also monitor crop harvests such as beets and potatoes.
“This is an inconspicuous AI and it should be. You don’t even know it’s there,” says Hertzberg. It includes small software modules built into the machine’s information technology and functionality.
This is an aid system that supports car-like drivers rather than making machines completely autonomous.
Siemers hopes that food stall AI applications will soon be present thanks to further developments in camera technology.
“There are already applications in the area of fattening in broilers to assess movement patterns in animals.”
As a farmer, he developed a sense of how animals do things when walking down the food stalls. “You get a sense of how animals behave and how they look when they’re doing well, and that’s what experience tells you.”
AI-based applications can be a key aid, says Siemers. They can process far more data than humans, so even the best laid chicken farmers could be far better.
“Computers can respond much faster than they can, and computers are watching everything.”
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