The term mixed reality commonly referred to as MR and artificial intelligence widely abbreviated as AI is today vital in farming leading to improved efficiency, productivity, and reduction of costs. With augmented reality and virtual reality technologies, Mixed Reality overlays the digital environment on the physical one to help farmers better manage their work by visualizing real-time data. On the other hand, AI applies big data analysis in a bid to provide recommendations for the best way to manage the crops.
In this post, you will see how MR and AI are revolutionizing agriculture and implementing efficient models for raising agribusiness.
What is Mixed Reality?
Mixed Reality (MR) is one of the enhanced technologies, which combine real and digital environment and make a single evolving world in which virtual and natural objects interact. And in this aspect, it integrates Augmented Reality (AR) and Virtual Reality (VR) to provide users the chance of not only assimilating the content visually, but also of interacting with this content in their physical life space.
In agriculture, MR is ready to revolutionize the farming processes from monitoring, management to the harvesting processes among others. To achieve this, farmers using MR can be able to acquire timely critical data volatile in the agricultural environment and hence fit timely and apt decisions on the agricultural processes. These changes, which have been linked with the new developments in production technology, hold great potential of taking efficiency, precision and sustainability in modern farming to levels that have never been seen before.
Machine learning in agriculture
Machine learning has impacted agriculture by using statistical models to reveal data behaviours and make forecasts. This advancement in technology helps in managing development tasks minimally as it does not require this complicated programme to be developed. However, in the case of computers, these issues are solved independently by the computers. Such evolution is critical in IT, particularly within agriculture, as AI holds a lot of value for the sector.
Developed in the 1950s with focus on chess-playing computers, machine learning has been continually adapting with power in computers. Today, it responds to the various issues, and some have to do with farming. Stemming from AI, it uses state-of-the-art techniques of intelligent technology.
In agriculture, machine learning works with sets of certain conditions because algorithms are trained using large amounts of data. They contribute to decision making procedures They provide information. For instance, the algorithm is capable of identifying the flower-containing images since the pictures are labelled as ‘Contains a flower’ or ‘No flowers’.
How Automated Farming Operates
Smart farming or automated farming is a new form of farming that refers to the application of artificial intelligence along with mixed reality in farming. We see it using sensors, drones, robotics, and all sorts of automation in the management of crops.
In this approach, there are devices which detect the information concerning the quality of the soil and its capacity to contain moisture, heat, and nutrients. AI processes this data to come up with a decision for matters concerning irrigation and fertilization, pests. Through applying those technologies, farmers increase production and utility and at the same time decrease expenses and negative effects on the environment.
Automated farming cannot be complete without drones as they feed images and other data benefiting farming through provision of information on crop health, pests, diseases either through visual images or other related data such as spraying fertilizers or pesticides. They help establish quick solutions in regard to field difficulties; this way farmers can easily control crop production.
Automation is also used where robots are used for planting and weeding, followed by picking and even sorting of fruits and vegetables. It also minimizes the use of labor, and this helps in efficient running of the farms, hence this automation.
Mixed reality which is virtual and the actual reality blended and integrated into producing resources for the farmers is one of the major automation inventions in farming. It helps in visualizing data, in the monitoring of crops, and in coming up with real time decisions. Using Mixed Reality such as Head Mounted Display or other equipment, farmers view their fields in 3D, diagnose problematic areas and experiment with potential improvement to their workflow and productivity.
MR and AI in farming
While the application of AI and MR are not common, there are cases that offer proof of the applicability of major technological advancements in agriculture.
Autonomous tractors
In 2012, the world got to see the first ever, completely self-driving, tractor designed specifically for agriculture. This revolutionary wearable prototype will employ radio navigation, laser gyroscope, and Artificial Intelligence. It is taught the routes through first showcasing the execution by an expert to accomplish specific tasks as in case of a novice farmer. Data collected by the tractor is captured in real time and transmitted wirelessly by a specific application downloadable on the mobile either developed or redesigned exclusively by IT companies.
Computer Vision in Farming
A new farming technology is a tractor fitted with a computer vision system. The tractor has integrations that have live cams, GPS, and even a computing block. Thus, this system can identify the presence of risky objects in the field and offer their sizes and coordinates to make maps. These facilities permit farmers to get rid of these objects before the harvesting stage to deter harm to equipment. The tractor with computer vision and other related farming apps that would be compatible with it are still under development and the tract with computer vision is expected to be in the market next two years.
AI-based smart irrigators
While modern irrigators with the help of machine learning and smart farming define not only the water supply but also chemical reels. They spot the weeds in a sea of healthy plants, then spray the herbicide with great accuracy. Plant capturing through the advanced software for AI analysis completes more than 5000 plants’ images per minute. Besides, it optimizes the process and decreases chemical consumption, which creates the basis for independent growth and yielding processes.
Satellites Enhanced with AI
Farmers keen on utilizing space technology can ease their working by doing the following. Harvesting is a new venture that has employed satellite information to determine corn yields. Satellites evaluate crop status with the assistance of Machine learning algorithms. This technology is crucial since money resulting from imprecise forecasts can be lost in terms of billions.
AI Plant Disease Diagnosis Services
Today, there are thousands of applications and sites that contain healthy plants and plants with diseases. The information gathering may also involve farmers posting pictures of affected plant to have AI have its algorithms identify the disease. This technology is efficient in the sense that it offers the right diagnoses which in the long run will spare resources. Such services are still under development but our company has experience in custom software development for agriculture and can help optimize such apps.
Chatbots for farmers
Chatbots are virtual helpers that are within your smartphone and with which you can interact. It applies artificial intelligence interfaces for interacting with the users. It is very clear that chatbots can be really helpful for farmers and, generally, for improving high-tech farming. There is a basis for farmers to seek answers or get advice on something thereby making the app dialogical. It is suggested that developing a farmers’ oriented chatbot that specializes in agricultural knowledge is ideal.
Conclusion
Combined with mixed reality and AI, farming as a process is expected to be transformed big time. All these technologies provide current information that fosters efficiency, guarantees lower expenditure, and raises sustainability levels. Farmers are now able to visualize the fields and decide on many aspects ranging from position of crops to requirements of fertilizers. Using AI Huge data is analyzed to understand the yields to be produced and in case of plant diseases too. Altogether, they redefine farming to be as being efficient and sustainable as possible. Adopting these changes hence fosters future outcomes of increased efficiency, including sustainable farming.
