Artificial intelligence is driving innovation across the agriculture industry, enabling farmers to detect crop disease, pests and livestock illness without ever stepping foot in the field. They can also access detailed soil analyses and AI-powered recommendations, eliminating any guesswork from decisions about what fertilizers, irrigation and chemical treatments to use.
How Is AI Used in Agriculture?
AI is used to identify pests, diseases, weeds and deficiencies in soil nutrients, providing data that helps farmers manage their resources more efficiently. It’s also used to manage livestock, and it’s programmed into farm machinery to more efficiently apply water, fertilizer and pesticides.
Overall, AI tools can help farmers save money, reduce their environmental footprint and maximize their crop yield. The latter will be increasingly important, as the United Nations’ Food and Agriculture Organization predicts food production must increase 60 percent by 2050 to feed an estimated global population of 9.3 billion.
In this article, we’ll get into the weeds and introduce you to all of the ways in which AI is being used in the agricultural sector.
Applications of AI in Agriculture
Detecting Pests and Diseases
Manually inspecting plants for signs of disease and pests is nearly impossible for large farm operations. There are several products on the market that utilize drones and cameras equipped with computer vision to get a closer look at crops. Machine learning models can identify patterns and anomalies in these images using image segmentation, a technique that divides each image into smaller groups for faster, more precise analysis.
Taranis, for example, provides high-resolution images of fields that allow farmers to zoom in with millimeter precision. It also alerts farmers to pests, diseases, weeds and nutrient deficiencies. Farmers can also take pictures of plants using AI-powered smartphone apps like PlantVillage Nuru and Plantix that diagnose diseases and pest issues.
Pest-specific applications like Trapview uses image recognition to classify and count pests to monitor and predict infestations. FarmSense, meanwhile, uses optical and acoustic sensors to detect the wingbeat frequency of insects, helping farmers make more targeted pest control applications.
Controlling Weeds
Weeds can crowd out desirable crops while diverting valuable water and nutrients away from them. Instead of blanketing entire fields with herbicides, John Deere’s See & Spray technology uses computer vision and machine learning to spray only on weeds, reducing herbicide usage by more than 50 percent. Another precision applicator, Verdant Robotics’ Sharpshooter, claims to reduce chemical usage by 99 percent and labor costs by 85 percent.
Several companies have also developed non-chemical means of eliminating weeds. Naio Technologies has developed autonomous robots that extract weeds using serrated disks and other mechanical tools. Carbon Robotics, meanwhile, has developed an autonomous weeder that zaps and kills weeds with 240-watt lasers without disturbing crops.
Monitoring Soil Health
AI can also ensure that crops have the exact nutrients and water they need to grow. CropX, for example, collects data from underground sensors that monitor soil moisture, temperature and other soil health indicators. It also gathers data from weather stations, satellites and above-ground sensors that measure evapotranspiration. Using AI-powered models to diagnose and predict issues, CropX can tell farmers how much they should water and what fungicides, herbicides or pesticides to apply for each specific crop.
Another product, Arable’s solar-powered Mark 3, transmits crop images and more than 30 data points — like soil moisture, leaf wetness and chlorophyll index — via cellular networks to its software platform, helping farmers make data-informed decisions.
Managing Livestock
Farmers use AI-powered sensors and cameras to monitor and analyze animals’ health, feeding habits, movement and milk production. One company, CattleEye, uses computer vision to analyze a cow’s gait and detect early signs of lameness. Another company, Ever.Ag, uses computer vision to detect early signs of cow pregnancy and reduce feed costs. Virtual fencing company Vence, meanwhile, allows farmers to set up digital perimeters within minutes, making it easier to rotate livestock between grazing areas.
Predicting Crop Yield and Market Demand
Predictive modeling can help farmers forecast their crop yields, take corrective action and make data-informed decisions. Bayer’s FieldView and Agmatix are just a few of the software platforms that analyze field metrics and historical data to make AI-powered recommendations. Taranis, meanwhile, has developed an AI agent that develops field-specific reports for farmers.
Farmers also use AI tools like Throughput to predict and plan for market demands. Commodities traders, meanwhile, rely on AI tools like CropProphet to predict crop yields across the broader agricultural market. Another company, AgFlow, uses AI to generate agricultural market forecasts for commodities traders.
Benefits of AI in Agriculture
Enables Precision Agriculture
AI is paving the way to what is known as “precision agriculture,” a data-centered approach to farming. Using information about soil health, crop diseases, pest infestations and yield estimates, farmers can make more accurate decisions about how to allocate their resources and maximize their crop yield. Variable rate technology, for example, helps farmers to be more precise in their applications of water, fertilizer and pesticides, which saves money and reduces their environmental impact.
Facilitates Regenerative Farming
Regenerative farming practices aim to replenish — rather than deplete — natural resources through environmentally sustainable practices. AI products can help farmers adopt regenerative farming by reducing water usage, minimizing pesticide applications and rotating grazing areas.
Challenges of AI in Agriculture
High Implementation Costs
Many of the AI-powered agricultural products we’ve discussed here are relatively new, and they are primarily designed for farmers operating at a large scale. While AI technologies can save farmers money on labor, pesticides and livestock feed — and increase yields in crops and livestock — the steep upfront costs may be difficult for smaller farmers to absorb.
Technical Barriers to Entry
In addition to the high price tags, the adoption of these products may be hindered by technological learning curves, long data collection periods and lack of technical infrastructure — most importantly, internet connectivity. As large agribusinesses get more efficient with AI, small farmers may find it increasingly difficult to compete in a market where the odds are increasingly stacked against them.
Frequently Asked Questions
How is AI being used in agriculture?
AI is used to diagnose crop troubles like pests, diseases and nutrient deficiencies, and it helps farmers more efficiently apply pesticides, water and fertilizer. It’s also used to monitor livestock, diagnose animal illnesses and control where they graze. These technologies can help farmers save money, maximize crop yields and adopt regenerative farming practices.
What percentage of farms use AI?
An estimated 87 percent of U.S. farms use AI, according to a 2021 report by analytics firm RELX. The data is mixed, though. A 2024 report by Continental found 79 percent of farmers rely on digital tools, but only 10 percent said they use AI.
What are the disadvantages of using AI in agriculture?
Many AI tools are designed for large-scale farms, so smaller farms may face steep costs and technical hurdles to adopt them. Some researchers have also warned about the risks of cyberattacks that disrupt commercial farming operations.
Will AI replace farm workers?
Farms are facing a major labor shortage, so most farmers are more worried about filling those gaps than displacing workers. Labor groups have mixed feelings about AI. They hope that AI can make farming jobs safer and more attractive, and they would like workers to have a say in how these technologies are implemented.
