Digitization is no longer a foreign word in many industries. The trends and terms that have been discussed there for some time are now spilling over into our industry with new digital solutions for agriculture. To clear up the jungle of terms a bit, we have explained 10 frequently used terms in this article.
The term Agriculture 4.0 is often used to describe the digital revolution on farms in arable farming but also in animal husbandry. Agriculture 4.0 thus means nothing other than the digitalization of agricultural production processes. This also includes the automation of work processes, the use of robots and sensor technology, as well as the use of mobile devices such as smartphones or tablets to control farm processes. In English-speaking countries, the terms smart farming, digital farming or e-farming are used synonymously.
Precision Farming and Precision Livestock Farming.
Loosely translated into German, 'precision farming' is the term for targeted agricultural measures, whether in the field (the origin of the term precision farming) or in animal husbandry (precision livestock farming). In the field, this primarily means the use of sensor- and GPS-controlled technology, e.g. for fertilizer application. In livestock farming, on the other hand, innovative technologies are expected to make process monitoring and control as well as animal care even more efficient in the future, enabling individual animal monitoring even in growing livestock populations.
Sensors, GPS, computers, smartphones - the technology with which we now collect, document and evaluate data is becoming increasingly diverse, and the result is clear: data - and at some point so much data that the naked eye or simple data processing techniques are no longer able to control the data or derive meaningful evaluations. This is called Big Data. The potential of this data is enormous. If it is linked correctly and combined into meaningful evaluations, it can support decision-makers in their daily tasks. In agriculture, the possibilities for accumulating Big Data are already there, but it is not yet happening on every farm and the networking between data-collecting systems is also insufficient so far (An article about possible concerns and problems on this topic can be found here).
The cloud refers to a storage space that can be accessed via the Internet and can thus be described as a data cloud. You can thus work on data or even entire programs without necessarily having to install them on your own device. At the same time, it is usually possible to access the data from different types of device, regardless of location. In agriculture, this is mainly used when the farmer needs to be able to access his data not only in the barn, but also in the office, on the field or elsewhere.
IoT is the abbreviation for 'Internet of Things'. Basically, IoT is a collective term for all technologies that connect technologies and things, e.g. sensor technology, via the Internet and thus allow things to communicate with humans and with each other. Field robots, drones and driverless tractors are already using this trend to some extent.
Blockchain technology is concerned with optimizing processes and entire value chains across multiple players, especially in terms of data security. A blockchain, or 'block chain' is composed of several connected blocks. These blocks contain data that is secured in that block with a timestamp, a code of the previous block and other transaction data. This increases the transaction security of data as well as transparency and traceability. Above all, this creates trust between the actors exchanging data and the susceptibility to data manipulation is significantly reduced. The application for agriculture is still in its infancy in German-speaking countries, but in times of increasing networking along the food production chain, it is a method that we will hear about more often.
ISOBUS is a special form of data bus and thus a system for data transmission between different participants via a common, in the case of ISOBUS even standardized, transmission path. It refers to the ISO 11783 standard, which specifies certain characteristics of networks, connectors and cables, data formats, interfaces, etc. for agricultural and municipal applications. This simplifies networking between different individual systems, saves you from having to deal with a tangle of different cables when installing new technology, and last but not least, it also promotes ease of use (More on this here).
Machine Learning is part of the concept of Artificial Intelligence (AI). An artificial system learns from collected data, examples, etc., in which it searches for patterns and recurring structures from which regularities can be formed. By networking the data of a farm over several years, machine learning algorithms can be trained to identify the success factors of the farm and thus help the farmer to make the right decisions. Furthermore, the algorithms can also be used to detect plant diseases, weeds or pests, for example.
RFID is the abbreviation for 'radio-frequency identification', i.e. identification by means of radio signals in transmitter-receiver systems. This means that the system always consists of at least one RFID chip, which can, for example, be in a cow collar and carries an identification number, and a read-out device, which is located, for example, on the milking carousel. According to top agrar, about 45 million RFID chips are in use in agriculture and pets. The main practical advantage of RFID technology is the tiny size of the chips and the fact that they usually do not require their own power supply - RFID chips are also included in newer ID cards, for example.