With sensors, apps and high-tech machines, Smart Farming expands the possibilities in the field and in the barn. We show how the data being collected here is revolutionising agriculture.
After around 7,500 years of agriculture and livestock farming, Central European agriculture is now in the midst of a new historical upheaval: with the networking of different machines, the use of cloud systems, big data and robotics, digitalisation is changing almost all the value chains that have been established to date. The list of technical solutions for Agriculture 4.0 is constantly growing: From drones that monitor fields, to robots that milk cows, to self-driving tractors. Even if many developments are still in their infancy: in a BitKom survey conducted among a representative sample of farmers, 53 percent of the farmers surveyed stated that they were already using digital applications in the sense of Agriculture 4.0, mainly in the form of automatic feeders and high-tech agricultural machines.
Data harvest for the cloud
Smart farming, i.e. automation with the help of digital systems in agriculture, saves farmers work and time, increases their efficiency and competitiveness, reduces environmental pollution and supports the sustainable use of plants and animals. In order to digitally network and optimize work processes, it is important for farmers to monitor a wide range of information, such as about soil, plants, animals and weather. This data must be collected, processed and analysed using intelligent software. This is also associated with increasing demands on data management and on data security and legal compliance.
Machine communication in the field
The basis for data collection in the field and in the barn are on-board computers, mobile recording devices, satellites and sensors. In the domain of field management, it is attaching sensors to agricultural machinery that has been the most widespread step so far. Sensors from tractors, for example, collect all the relevant data that is generated during your work in the field. Nitrogen sensors can use light sources to measure leaf colouring and then distribute fertiliser to specific areas. The measurement data from various sources can be collected and analysed. As a result, for example, exact "application maps" for fertiliser and seed can be sent back to a computer in the driver's cab. The spreader attached to the tractor receives real-time commands for the precise application of pesticide and fertiliser. Using a cloud system, different pieces of equipment can communicate with each other and optimally supplement their work steps – the farmer keeps track of things in real time using the software and can control the processes more efficiently. Precise field management is also made possible by geodata-based steering systems and correction signals on high-tech agricultural machines that stay exactly on track and operate with centimetre precision.
Sensor-based yield forecasts
Agricultural drones can also be used for field analysis. Aerial photographs and special "multispectral cameras" mounted on drones detect irregularities in the field and assist the area-specific treatment of the field. Similar functions are performed by sensors on field robots currently being developed. They drive autonomously across fields. Using the data from sensors, in future they will be able to remove weeds in real time. More and more data for agriculture will also be collected from outer space. Satellites survey the relevant fields and transmit precise data on the condition of crops, the moisture content of arable soils and the expected yield. The software then analyses this information. The farmer can take this as a basis for yield forecasts, to assess yield risks and cases of damage and optimise the use of fertilisers and pesticides.
Smart data in the barn
The progress made in digitalisation has also found its way into barn planning. The best known smart farming application in livestock farming is the automatic milking system. It uses laser, ultrasound and camera sensors to connect the milking harness to the cow's udder without manual assistance. A sensor also detects when the milk flow stops and gives the harness the signal to detach from the udder. Milking robots not only accelerate the milking process, but also increase the milk yield by an average of seven percent. There are also digital programs to monitor individual animals: sensors attached to the animal's ear or collar measure a variety of data that is analysed and passed on to a monitoring program in the computer, tablet or smartphone. Farmers receive daily information on the weight, eating and movement behaviour or milking time and milk quantity of each individual animal. Sensor-assisted monitoring also measures changes in behaviour and detects illness, heat for breeding, births or problematic situations at an early stage. This allows the farmer to react immediately in an emergency. The fact that process data from automated systems also benefits animal welfare is demonstrated by feeding solutions: machines compose the feeds together to the second decimal place, thereby ensuring optimum feeding.
Other sensors in the barn record the process data of technical equipment such as automatic feeders and milkers. Brightness and climate can also be adjusted using sensors, for example by means of intelligent blinds. To clean the barns, robotic systems often use ultrasonic sensors to measure distances to walls, stalls and anything else that moves. This allows them to clean the entire barn without tipping over and without disturbing the animals while eating, sleeping or resting.
Expanding and stabilising digital infrastructure
The future mass communication from sensors is highly dependent on the quality of the Internet, as the collected data is delivered to an agricultural analysis system via the cloud and processed there. Sending agricultural data to the cloud and back is only possible with a fast and extensive Internet, with maximum reliability and low latency. In addition, software and analysis systems must be designed to function in the event of a crisis or hacker attacks. Data security and data sovereignty must also be ensured – after all, this is operational data that is being transferred. A solid basis of trust between service provider and farmer is therefore vital.
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