Five Useful Applications of AI in Agriculture

now a cutting-edge technology in the field of agriculture. Through improved agricultural yields, insect management, soil monitoring, and other means, it benefits farmers. Some significant uses of artificial intelligence in the agricultural industry are listed here:

Weather and Price Forecasting
As we covered in the section on problems, farmers find it difficult to make the best choices for harvesting, planting seeds, and soil preparation due to climate change. However, farmers may plan the sort of crop to produce, the seeds to sow, and the crop harvesting process with the aid of AI weather forecasting, which provides them with information on weather analysis. With the use of price forecasting, farmers may have a better understanding of the price of crops over the next weeks, enabling them to make the most money possible.

“Only when the nature’s intelligence and artificial intelligence are married together, will an exponential shift be seen- substantially increased farmer income, environmental sustainability, and better consumer health,” says Agam Khare, Founder and CEO of Absolute.

Crop health surveillance
The kind of soil and nutrition of the soil have a big impact on the quality of the crop. However, because of the speedy rate of deforestation, it is difficult to gauge how well the soil is doing. Plantix – a mobile crop doctor app is a new application that has been created by PEAT (Progressive Environmental & Agricultural Technologies) – an age-tech start-up to address this problem. It detects soil inadequacies, such as plant pests and diseases. Farmers may learn how to utilise fertiliser better with the aid of this application, which will enhance the quality of their yield.

Robotics in agriculture
Robotics is frequently utilised to carry out complicated tasks across a variety of industries, mostly in manufacturing. Numerous AI companies are now creating robots for use in the agricultural industry. These AI robots were created in a way that allows them to do a variety of farming jobs. AI robots are also taught to detect and eliminate weeds, inspect crops for quality, and harvest harvests more quickly than a person.

Medical diagnosis
Using AI predictions, farmers may readily learn about illnesses. With this, they can quickly and effectively identify illnesses. It can spare both plants' lives and farmers' time. First, computer vision technology is used to pre-process photos of plants. This guarantees that plant photos are appropriately segmented between the healthy and unhealthy sections. The infected area is cut off after discovery and sent to the laboratory for further diagnosis. This method also aids in the detection of pests, vitamin deficiencies, and many other things.

Precision agriculture
"Right place, Right time, and right goods" are the three pillars of precision farming. The labour-intensive aspect of farming that involves performing repeated chores may be replaced by the precision farming technology, which is considerably more precise and regulated. The measurement of plant stress levels is one instance of precision farming. High-resolution photos and other plant sensor data can be used to get this. The machine learning model used for stress identification is then fed the sensor data as input.

The Way Forward
The adoption of AI solutions will have a significant impact on the future of AI in farming. Even though some extensive studies are ongoing and certain apps are currently available on the market, the agricultural business still needs more support. Additionally, the development of predictive solutions to address a genuine problem encountered by farmers is still in its early stages.