As an industry, agriculture has evolved since prehistoric times. With food supply, this industry has catered to one of the basic needs of humanity. Presently, the digital transformation in the form of AI, IoT and machine learning has been transforming every segment.
Integration of AI in the agricultural sector has been changing the way food is produced and distributed. FAO, the food agencies of the United Nations, states that across the world, one-third of the total food produced is actually wasted. AI-generated data, obtained using agricultural drones and sensors from farms offer possible remedies to deal with the issue.
Reputed AI developers have come up with sophisticated mechanisms that can bring about a new paradigm in agriculture. Information about seeds, soil, costs, livestock, farm equipment and the use of fertilisers can be analysed to gain valuable insights.
Eventually, the production yield can be optimised, and the authorities can carry out a sound planning. AI technologies in agriculture can help governments make smarter choices, pertaining to the quantities of resources required. They can also decide where and when they can be distributed, so as to prevent wastage.
AI integration in agriculture: How are farms optimising the production level?
Estimations reveal that around 75 million IoT devices will be in use in the agricultural sector by 2020. On an average, a farm is likely to increase the data points from 190,000 in 2014 to 4.1 million in 2050. In agriculture, cognitive computing is expected to be one of the most disruptive technologies, yielding positive results.
Here are some of the benefits that the agriculture industry is already reaping from AI.
- Research and development
The scope of R&D in agriculture using AI technologies is immense. Like the pharmaceutical industry, AI can help agriculture reduce the duration of trial and error. Eventually, the R&D process is becoming less time-intensive and accurate. For instance, certain AI algorithms have been developed to determine the hybrid plants that would be ideal for real-life conditions. As a result, a lot of time for product development can be saved.
- Image recognition
In some parts of the world, farmers are using agricultural drones to monitor crops by scanning the fields. They can evaluate the health of the plants and monitor seeding. With computer vision technologies and drone data optimising production strategies in agriculture, the output is likely to increase.
Leading AI developers are collaborating with IT firms to develop customised technologies. These are being designed to capture and analyse images in real-time. They can also mark out the areas of concern and take necessary actions to address the same. With more upgraded AI systems coming in, agricultural firms will be able to increase safety, save time and mitigate human error. In the process, it will enhance efficiency in agriculture.
- Workforce and skills
According to a report by the UN, 66% of the global population will be residing in urban areas by 2050. As a result, workforce in the rural areas will be greatly decreased. It is important to develop cognitive systems using AI and other innovative technologies. As a result, the work of the farmer will be eased up significantly. In the coming years, it is expected that the number of people working on the farm will be reduced.
At the same time, automation in agriculture, using AI technologies, will enable the farmers to carry out the tasks remotely. Besides, AI technologies will effectively identify potential hazards and resolve the issues before they actually occur. Farmers will also be able to make more rapid and informed decisions.
Eventually, the right blend of AI technology and skills will optimise productivity in agriculture.
- Use IoT to its full potential
AI and IoT are two of the technologies, which have been shaping all the industries in the digitised age. In fact, AI has got the power to help IoT achieve its full potential. Machine learning abilities are being used to evaluate drone data. AI systems enable the analysts find correlations among large volumes of data, both structured and unstructured.
This information comes from a multiplicity of sources, like historic data on weather, research notes, posts on social media, market information, soil information and images. This can help extract valuable insights and the organisations dealing with agriculture can benefit from the recommendations, improving the yields with the right actions.
- Maximising ROI on crops
With AI technologies around, agricultural firms can determine the most appropriate methods, through which they can maximize the ROI on crops. For instance, AI technology can help farmers determine choose the right type of crop. Eventually, they can determine the right mix of crops that are customised for various objectives, needs and weather.
AI technologies also provide important insights on how a particular seed will react to a particular type of soil, local conditions and weather forecasts. On correlating and analysing data about the type of seeds, type of soil and weather, the year-to-year outcome can be optimised.
At the same time, AI technologies can also determine the probability of diseases. Eventually, the farmers can maximise their ROI on crops.
Virtual assistants in the form of chatbots have been developed specifically for farmers. The interactions can be automated with the end-users. These chatbots are powered by AI, where machine learning techniques are integrated. The chatbots are also powered by natural language processing, so that the users can enjoy a personalised experience during the interaction.
Although chatbots are presently being used by retail, media, travel and insurance companies, it has a great potential to leverage agriculture. Farmers can gain the necessary assistance, getting all their queries regarding agriculture answered. AI-integrated chatbots can also provide recommendations and advice on particular farm issues.
The integration of AI in agriculture has been relatively late. However, these cognitive technologies have an exciting time ahead, leading agriculture towards greater sustainability and efficiency. This can actually meet the food requirements of the world, particularly in the developing and third-world countries. It remains to be seen how agribusinesses, farmers and other players of the industry harness the potential of AI.