What is Mixed Reality?
Mixed Reality (MR) is a term gaining its popularity today. It defines a combination or union of two virtual environments where two worlds coexist together. Sometimes a mixed reality is called a hybrid reality.
Let's discover some mixed reality examples. It makes it possible to simultaneously investigate a virtual environment and real world as a single whole. Using real environment and coordinates virtual objects can be placed in a real world. When a user approaches a certain object, it is enlarging, when moves away - it is shrinking. Owing to a virtual reality, users can explore an object from different angles and at any distance. Besides, a mixed reality allows users to affect virtual object and interact with them as if they were in the same place.
Mixed reality can turn your smartphone into an interactive handbook where the informational environment for places we are located in is building on. How does MR technology affect farming? 3D-mapping technology makes it possible to turn fields into the virtual environment where farmers can generate different scenarios of crop cultivation, even if they seem fantastic in real life. Special software combined with webcams in a virtual environment augment physical objects as well as integrate us into the virtual world.
Owing to unique capabilities and experience a hybrid reality has, developers start investigating different ways of efficient use of this technology in diversified areas. Present-day medicine, architecture, education, and smart farming are the most advantageous fields for the application of a hybrid reality technology.
Machine learning in agriculture
Machine learning - a complex statistics application for search of consistent patterns in data and development of required forecasts - has eased the process of a task assignment. Developers do not have any longer to build special programs for their computers to solve one task or another. Instead of this, a computer is taught to find the problem by itself, without any assistance. A real breakthrough in the world of information technologies. And, considering technical capabilities of AI, agriculture field cannot be ignored.
History of machine learning has begun in the 1950s when computer scientists managed to teach a computer to play chess. Since then, together with computing capacity, the complexity of consistent patterns and forecasts computer is capable of drafting and detecting has been growing. As well as the complexity of problems computer can resolve today, and farming problems are also included in this list. Moreover, machine learning is a subdivision of artificial intelligence (AI), so complex methods of smart intelligence are applied in ML technology.
How does ML work in farming practices? The algorithm gets a range of training data and then uses it for requests processing. For example, you can upload in the computer a few pictures with the description like 'A flower is depicted on the image' and 'There are no flowers at this image'. If you add new images to the database after this, it will start identifying pictures with flowers on its own.
The algorithm keeps on improving. Right and wrong results of an image recognition are sent to the database, and software is becoming smarter with every processed image. In some sense, such process can be compared to building a muscle - the more you train, the stronger you get. The more images you have downloaded in the program, the more precise result it will produce.
Thus, AI and machine learning, in particular, can significantly change the agriculture and the whole smart farming field. How? The answer is waiting for you.
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