Drones, robots and Artificial Intelligence – farmers are increasingly being supported by high-tech in field work. According to a Bitkom study from April 2020, eight out of ten farms in Germany already use digital technologies. This saves work, is good for the environment and increases the yield.
Like busy bees or useful ants, they stream out in swarms, flying or driving from plant to plant and not resting until they have done their work. But while bees and ants have populated the earth for about 100 million years, they have only existed for a short time: drones and field robots. Equipped with state-of-the-art technology and software, they can carry out field work autonomously and thus become an important part of smart farming, of intelligent agriculture. Numerous companies are actively fostering the development of these promising "smart" little helpers.
"For about a hundred years, agricultural machines have been getting stronger and bigger," explains Benno Pichlmaier, Head of Fendt Pre-Development. "As far as weight and size are concerned, we will soon reach limits (for physical reasons and traffic law regulations). So what does the future look like? Figuratively speaking, we are facing the following development: A tractor grows and grows, explodes and disintegrates into nothing but small parts. From these small parts, which are equipped with intelligent technology and developed further, a swarm is formed which then brings even more efficiency to the field". The approach of the Fendt engineers: For example, the farmer plans the sowing of maize on the computer, uses a logistics unit - such as a special trailer - to transport sowing robots to the edge of the field. The robots swarm out and do the sowing autonomously. Nothing is left to chance, as they are precisely controlled by the cloud and via GPS satellites. If the robots need new seeds or electricity, they drive back to the logistics unit independently and load.
The autonomous helpers are also beneficial in controlling weeds and pests. For example, a weeding robot can mechanically remove weeds. By means of Artificial Intelligence it can distinguish between useful plants and weeds. This is not an easy task. For example, the leaves of carrot and camomile are similar in the early stages. With the help of innumerable image data, weeding robots gradually learn to differentiate between plants on the basis of several parameters such as leaf colour, shape and size. Progress has also been made in the control of the European maize borer, a pest which can cause significant economic damage to maize production. For example, the Bavarian State Institute for Agriculture expects yield losses of 10 to 30 per cent with an infestation rate of two to three caterpillars per plant on a long-term average. For this reason, drones deploy capsules with ichneumon flies over the field at regular intervals. These destroy the maize borer's eggs and thus fight it effectively, and environmentally friendly. In this way, maize yield and crop quality can be successfully safeguarded.
It will still take some time before field robots and drones will be deployed throughout the country, despite the successes achieved in combating the maize borer. According to the Bitkom study, less than half (46 per cent) of the surveyed agricultural holdings plan to use field robots by 2030. In addition to drones and robots, large machines will therefore continue to be used. They are particularly useful during the harvest. Combine harvesters can bring in large quantities of grain in a short time and powerful, high horsepower agricultural machinery and trailers with a capacity of several thousand litres can quickly transport the harvest to the farm. But here too, smart farming plays a major role.
Whereas farmers used to spend hours sweating in midsummer temperatures whilst harvesting their crops, many now sit in air-conditioned, low-noise, high-tech cabs. An on-board computer monitors and controls all processes, from yield measurement to precise tracking, based on previously collected data. By networking and analysing various data, recommendations for action can be derived and work can be implemented in parts automatically.
For data collection, on-board computers, mobile recording devices, satellites and sensors are used. Tractor sensors, for example, collect all the relevant data generated during work in the field. And not only during the harvest. Nitrogen sensors can use light sources to measure the colour of leaves and thus distribute fertiliser to specific areas. Software compiles and evaluates the measuring data from the various sources. Based on the results, exact application maps for fertiliser can then be created and sent back to the computer in the driver's cab. A spreader attached to the tractor receives commands for precise delivery of fertilisers. The cloud also allows different devices to communicate with each other and coordinate their work steps. The farmer receives appropriate information about the processes in real time. In this way, he maintains an overview and can control work steps efficiently.
The advantage: Plants can be fertilized and irrigated as required in specific areas. The sowing density can aslo be varied to best suit the soil. This increases the yield and operating resources can be saved. This is beneficial for both the farmer and the environment.
