How AI Can Help In Agriculture — Applications and Use Cases

How AI can help agriculture - application and use case

Artificial Intelligence (AI) is expanding its footprint on the Earth's surface, making a significant impact on the world's most important sector - agriculture. After the healthcare, automotive, construction, and finance sectors, artificial intelligence in agriculture is now providing state-of-the-art technology for harvesting with good production and crop production.

The agricultural sector is the backbone of the world economy and with a growing population, the world needs to produce 500% more food by 2050. AI-enabled technologies can get farmers off the land more sustainably when using resources. Here we will learn how AI can be used to cultivate agriculture and its uses.

AI applications with use cases in agriculture and farming

Autonomous tractors

With huge investment in the development of autonomous vehicles for different needs, the agricultural sector can also benefit with self-driving or you could say driverless tractors.

With more quality AI and machine learning training data for agriculture, the large-scale use of autonomous tractors to perform multiple tasks will revolutionize the agricultural sector.

These self-driving or driverless tractors are programmed to independently locate their plow positions in the fields or to determine speed and avoid obstacles while performing various tasks such as irrigation objects, humans and animals.

Agricultural robotics

Similarly, AI companies are developing robots that can easily perform many tasks in the field. Such robotics machines are trained to control weeds and harvest crops faster, with higher amounts compared to humans.

These robots are able to check the quality of crops and detect unwanted plants or weeds and fight with other challenges faced by the labor force when picking crops.

Companies such as Blue River Technology and Harvest CRO Robotics are building robotics machines that can control unwanted crops or weeds and help farmers pick or pack more crops.

Controlling pesticide infections

Pests are one of the enemies of farmers who damage crops globally and from which they are stored for human consumption. Popular insects, such as locusts, ticks, and other insects, are profiting from farmers and feeding on grain used for humans. But now on the farm, AI gives producers a weapon against such bugs.

AI and data companies are helping farmers to be vigilant on their smartphones as garbage can move to a particular farm or mature crop area.

AI companies use new images using the same images as historical data and the AI ​​algorithm detects that the insects had moved to another location and farmers confirm such information and remove the expensive insects from their fields.

Soil and crop health monitoring

Deforestation continues and declining soil quality poses a major challenge for food-producing countries. But now a German-based tech startup PEAT has developed an in-depth education-based application called Plantex that can identify potential soil deficiencies and nutrient deficiencies, including plant pests and rogue.

This application is working on image recognition based technology and you can use your smartphone to capture the image of the plant and detect errors in the bots. You will also find soil restoration techniques along with tips and other solutions in short videos in this app.

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Similarly, Trace Genomics is another machine learning based company that provides soil analysis services to farmers. Such applications help farmers to monitor the health condition of the soil and crops and produce healthy crops with high levels of yield.

Sky Square Technologies, acquired by another similar company, Vineview, brought drone-based aerial imaging solutions to monitor crop health. A drone is used to create a round of data capture from a vineyard area and then all data from the drone is transferred to a computer via a USB drive and analyzed by experts.

The company uses algorithms to analyze the captured images and provides detailed reports, including the current health of the vineyard, usually grapes leaf conditions when these plants are highly deficient, such as molds and bacteria, which help farmers control them in a timely manner. Control and other methods.

Precision farming with predictive analysis

AI applications in agriculture have expanded to provide farmers with proper guidance on maximum planting, water management, crop rotation, timely harvesting, nutrient management, and pest attack.

Using machine learning algorithms in relation to images captured by satellites and drones, AI-enabled technologies predict weather conditions, analyze crop stability and evaluate farms for the presence of diseases or pests, and temperature, precipitation, wind speed, and solar radiation on farms.

In agriculture, AI has not only helped farmers automate their farming but has shifted to precision farming for higher crop yields and better quality using fewer resources.

Companies involved in improving services such as machine learning or AI-based products or training data for agriculture, drones, and automated machine building will make technological advances in the future providing more useful applications in this area to help the world deal with food production issues. Growing population.