Enhance Agri-Business by Mixed Agriculture mixed reality and AI – Signal of change to Smart Farming.
Agriculture Mixed Reality:
Agriculture Mixed Reality (MR) is a term increasing its popularity day by day. It defines a combination or union of two virtual environments where two worlds coexist together. Sometimes a mixed reality is called a hybrid reality.
It makes it possible to simultaneously examine a virtual environment and the real world as a single whole. Using real environment and regulates virtual objects can be placed in the real world. When a user addresses a certain object, it is enlarging, when moves away – it is shrinking. Owing to virtual reality, users can investigate an object from various angles and at any distance. Besides, a mixed reality acknowledges users to affect virtual object and communicate with them as if they were in the same place.
Mixed reality can transform your smartphone into an interactive handbook where the informational environment for places we are located in is increasing on. How does MR technology affect farming? 3D-mapping technology performs it possible to turn fields into the virtual environment where farmers can create different scenarios of crop cultivation, even if they seem incredible in real life. Special application combined with webcams in a virtual environment increase physical objects as well as merge us into the virtual world.
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.
Read More: Solve big problems of agriculture
Machine learning in agriculture
Machine learning – a complex statistics application for exploration of consistent patterns in data and development of required forecasts – has eased the method of a task assignment. Developers do not have any longer to build unique 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 trained to teach a computer to play chess. As well as the complexity of difficulties 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.