10 real-life examples of AI in the agriculture industry

How AI can aid agricultural practices

As Peña showcases on his farm, farmers can use AI for numerous tasks with multiple benefits.

Like in other industries, AI adoption can automate manual tasks to create efficiencies, reduce waste and identify opportunities to improve operations.

Thomasson pointed to the use of AI in autonomous harvesting, where AI can recognize and direct the end effector -- a tool that interacts with the environment, found at the end of a robotic arm -- to pick an apple or a cotton boll. AI with image analysis can identify the crop in a tree or field that the machine needs to harvest. Other algorithms can direct the action.

There are different types of AI for agricultural use cases, Thomasson explained. For example, some tools use discriminative AI, which classifies data by learning the boundaries between different categories of things. Some tools use machine learning, and others use generative AI.

As in other sectors, end users -- in this case, farmers -- receive most AI capabilities as part of technology products and services, said Bill Ray, VP analyst and chief of research at research firm Gartner. For example, equipment manufacturers incorporate AI into their tractors and other farming machines.

Many AI uses in agriculture fall under the umbrella of precision agriculture, Thomasson said. Precision agriculture uses technology to pinpoint specific needs, determine where and to what extent to optimize resource use, improve crop yields and minimize environmental effects.

For example, AI can analyze images to determine crop health, pinpoint locations that require pesticides, and decide which crops to plant where and at what density, Thomasson said.

The benefits of AI also help the environment by reducing carbon emissions from farm-related activities, cutting down on water use, limiting the use of herbicides and pesticides, and improving soil health, said Salar Nozari, assistant professor at the University of Iowa's Tippie College of Business.

AI in agriculture also brings greater crop yields using fewer resources, as well as reduced crop loss, Nozari said. These are critical gains as the world's population continues to grow and needs more food to sustain it.

10 examples of AI in agriculture

Here are ten real-world examples of AI in the agriculture industry:

1. Crop monitoring

DDrones and satellites capture real-time images of fields and crops, which computer vision algorithms analyze to determine plant health, detect stress, assess crop density, monitor growth stages and identify pest infestations, Thomasson said. This information can help direct attention to specific areas.

2. Yield forecasting

Algorithms analyze historical data, weather patterns, soil conditions and crop genetics to predict yields, Nozari said. AI output enables farmers, along with policymakers and industry leaders, to make data-driven decisions on what actions to take.

3. Autonomous machinery

Much of the farm equipment today uses GPS and AI, enabling tools to work autonomously, Ray said. Examples include self-driving tractors, harvesters and planters.

Robots are also limited in agriculture. The use of autonomous machines reduces the need for manual labor, which improves efficiency, lowers costs and boosts consistency in operations.

4. Pest and disease detection

AI algorithms analyze data from various endpoint devices, such as sensors and drones, to detect signs of disease or pest infestation, even at the earliest stages, Ray said. He explained that farmers use AI to model how pests or diseases might spread, providing them with more information on how best to contain the problem.

As is the case with crop monitoring, AI for pest and disease detection enables farmers to apply pesticides and other treatments with precision. In turn, this minimizes the use of resources, which helps control costs and limit adverse environmental effects.

5. Weed detection and management

This use of AI works similarly to its application in pest and disease detection, offering the same benefits. In addition, Ray noted that there are now AI-enabled robots in limited use for picking weeds, which can eliminate the need for pesticides while preventing plants from stunting crops.

6. Soil health analysis

Using data from various sources, such as sensors and satellite imagery, AI algorithms analyze and assess soil composition, moisture levels, pH and nutrient content, Peña said. Machine learning models then recommend strategies on planting, fertilization and management techniques to ensure the sustainability of the soil and/or optimal yield.

7. Supply chain insights and optimization

AI takes numerous data points, from cost predictions to market trends, to predict market demands, pricing and resource requirements, Ray said. He noted that agriculture industry leaders, policymakers, farmers and even food manufacturers use AI for supply chain insights and optimization. This, Ray explained, helps farmers and others better manage resources, reduce food waste, ensure profitability and prevent disruptions in farm production.

8. Farm management and business administration

Many business applications now incorporate AI to assist users with various administrative tasks, including budgeting, invoicing and strategizing -- all of which help farmers increase time efficiency, Peña said.

9. Climate risk assessment

Industry associations, government officials, researchers and farmers use AI models to understand how weather trends, climate and climate change affect specific growing regions -- including down to specific fields, Ray said. This enables stakeholders to make more informed decisions about which crops to plant where, which policies to enact, how to price insurance and what's needed for disaster preparedness and risk mitigation.

10. Digital twins

A digital twin is a virtual representation of a real-world entity or system, such as a forest, a field of crops or an entire farm. It uses real-time data to mirror the real-world entity and its current state, enabling simulation and analysis to explore what-if scenarios.

Although more common in manufacturing, digital twins are increasingly being used in the agricultural industry. Digital twins could use AI to determine the optimal planting patterns, harvesting and water use, Nozari said.

Challenges to AI adoption in agriculture

Despite AI's benefits to farmers, it's far from ubiquitous in the industry due to several challenges.

To start, the cost of AI capabilities -- and the cost of technology in general -- is too high for many farmers.

While some small farms might struggle to buy AI technologies outright, they can often afford to buy AI technologies as a service, Thomasson said. That doesn't work everywhere, though, Ray explained. These high costs exclude farmers in the developing world.

"The people who can most afford AI are the ones who need it the least, that being those in the U.S. and Europe, where agriculture is already highly mechanized and very efficient," he said.

That could change in the future, Ray added. As AI technology matures, costs should decrease, making it more accessible to farmers worldwide.

Other challenges can also slow adoption. The CAST report identified data quality issues, the limited applicability of AI models, connectivity gaps, data privacy concerns, resistance to change and a shortage of an AI-skilled workforce as key factors hindering AI adoption.

Additionally, some farmers do not have the IT infrastructure, data and digital maturity to make use of AI, Nozari added.

Despite these obstacles to adoption, AI-enabled innovation in farming will continue. According to a report by Roots Analysis, the AI in agriculture market size was $2.14 billion in 2024 and is projected to reach $20.96 billion by 2035, representing a compound annual growth rate of 23.06%.

"We will definitely find new ways to use [AI]," Ray said.

The CAST report echoes that sentiment, noting that although there are challenges, obstacles and concerns, "opportunities for the growth of AI in agriculture and its potential for positive effects on the industry are immeasurable."

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.