Computer vision in agriculture is rapidly changing how we grow, monitor, and manage food production. By combining AI-powered
agricultural imaging, robotics, and automation, farmers can make data-driven decisions that improve yields, reduce waste, and use resources more efficiently. From
autonomous farm equipment to precision livestock monitoring, the use of agriculture computer vision is helping feed a growing world amid rising environmental and economic challenges.
In this blog, we’ll explore key challenges in agriculture, leading applications of computer vision, and the companies driving innovation in this space.
Key industry challenges in agriculture
The world’s farmers have a monumental task, feeding a growing global population while also ensuring they don’t deplete the land they rely on for their livelihoods. Many farmers are turning to
computer vision in agriculture to drive innovations and unlock efficiencies to achieve goals against a backdrop of modern challenges. Before we dive into several popular applications of computer vision in agriculture, here are some of the main industry challenges facing agriculture that computer vision and AI can help with.
- Growing worldwide population: The number of humans is expected to reach 9.8 billion by 2050, leading to dramatic increases in food demand.
- Reduction in arable land: The amount of arable land on Earth is shrinking, with some studies suggesting that farmable land could be halved in the next quarter century.
- Shrinking workforce: The number of people working in agriculture is falling, from 40% of global workers in 2000 to just 27% of global workers in 2019.
- Increase in climate-related disruptions: The increasing frequency of extreme weather events is expected to lead to decreased crop productivity.
- Pest damages: According to the Food and Agriculture Organization (FAO), up to 40% of all crops worldwide are lost due to pests. Damages from plant diseases alone total $220 billion per year.
In other words, the agriculture industry needs to feed far more people with fewer resources in a complex and changing environment. The invention and adoption of new technologies is crucial to overcoming these challenges. AI and computer vision in agriculture are already
valued at more than $1 billion annually, and is projected to reach $2.6 billion by 2026. In computer vision applications in agriculture account for a large portion of this existing market, as well as the majority of its anticipated growth.
Applications of computer vision in agriculture
Computer vision precision in agriculture
With escalating prices for pesticides, herbicides, and seeds, precision agriculture through computer vision is helping farmers reduce their costs and get more from their land. As the name suggests, precision agriculture is all about finer-grained control over existing processes, from the placement of crops, to the constitution of the soil, to the application of chemical agents. Computer vision in agriculture is coalescing with robotics and other emerging technologies to bring a new level of precision to the field.
In precision agriculture, computer vision techniques are used in the field to make real-time decisions. Object detection techniques are used to identify and localize individual insects and weeds for the application of pesticide, and herbicide, respectively. Precision agriculture also has nonlinear regression models based on the coloring, size, and other visual attributes of plants that can predict exactly what quantity of each chemical the plant should receive. This means optimizing returns while also conserving resources.
Computer vision precision livestock farming
Precision livestock farming, or PLF, uses agricultural imaging to gain fine-grained insight into and achieve precise control over processes involving cattle, sheep, and other livestock. PLF can be applied for the purposes of maximizing yield, monitoring or ensuring animal health, or decreasing operational carbon footprint.
Computer vision agricultural imaging techniques are often used in conjunction with GPS tracking and audio signals to generate insights. Together, these techniques can be used to not only identify and track individual animals, but also to analyze their volume, gait, and activity levels.
Autonomous farm equipment
Autonomous farm equipment represents one of the most transformative frontiers of computer vision in agriculture. With advances in robotics, AI, and machine perception, farmers can now automate field operations, reduce labor costs, and increase safety.
Modern tractors, harvesters, and sprayers are equipped with high-resolution cameras, LiDAR sensors, and GPS systems that enable robot guidance and control.
Computer vision models for object detection, segmentation, and depth estimation help these machines recognize crops, detect obstacles, and navigate complex terrain with centimeter-level precision.
As agricultural computer vision continues to advance, autonomous farm equipment will play a vital role in addressing labor shortages, while helping to maintain sustainability at scale.
Computer vision crop monitoring
To combat crop loss, farmers use data from suites of soil sensors, localized weather forecasts, and multi-level imagery to remotely monitor large tracts of land. This data can be synthesized into “crop intelligence” allowing farmers to take informed action before it is too late. On the computer vision side, agricultural imaging from satellites, drones, and high-resolution cameras are used for early disease detection and monitoring, soil condition monitoring, and yield estimation.
By leveraging AI-powered computer vision, this agricultural imagery can be analyzed to detect subtle visual changes invisible to the human eye—such as early signs of nutrient deficiency, water stress, or pest damage. Deep learning models trained on large-scale agricultural imaging datasets can identify crop diseases with remarkable accuracy, enabling interventions that can prevent widespread loss. When combined with time-series analysis and predictive modeling, these systems not only assess current crop health but also forecast future yield trends, helping farmers plan harvests, allocate resources, and optimize overall farm performance.
Computer vision plant phenotyping
Climate change will subject many plants to increased temperatures, higher levels of carbon dioxide, and more variable precipitation. It will also make extreme weather events far more common. Some plants will be better equipped than others to survive and thrive.
Plant phenotyping is the process of identifying and understanding how genetic and environmental factors manifest physically, in a plant’s phenome.
One of the most important uses of computer vision in agriculture is becoming an important tool in what is widely recognized as a
key to global food security, with non-invasive detection, segmentation, and 3D reconstruction techniques giving researchers detailed information about everything from leaf area to a plant’s nutrient levels and biomass. As an example, segmentation of nuclear magnetic resonance images can be used to map the structure of a plant’s three dimensional root system, which plays an important role in the flow of water and nutrients.
Computer vision grading and sorting
After produce is plucked from the field, and before it ends up in the fresh food aisle of your local grocery store, it may be subjected to quality control processes. For fruits and vegetables, this takes the form of grading and sorting based on size, shape, color, and other physical characteristics. For grains and beans on the other hand, similar sorting processes are used to detect defects and filter out foreign material.
While grading and sorting were traditionally performed by hand, computer vision is now helping humans with much of this work.
Optical sorting uses image processing techniques like object detection, classification, and anomaly detection to incorporate quality control into food production and preparation. By 2027, the optical sorting market is
expected to surpass $3.8 billion. Grading and sorting is expected to be one of the biggest use cases for computer vision in agriculture.
Companies at the cutting edge of computer vision in agriculture
Carbon Robotics
Founded in 2018 and headquartered in Seattle, agricultural robotics startup
Carbon Robotics has raised $35.9 million to help farmers wage war against weeds with lasers, rather than whackers. The company’s
LaserWeeder technology uses the thermal energy in 30 onboard lasers to target weeds without harming crops.
The LaserWeeder uses object detection to identify and precisely locate weeds. High resolution images taken by mounted cameras are fed through an onboard Nvidia GPU and predictions, generated in milliseconds, are communicated to the lasers for firing. When attached to a tractor, this implement is able to eliminate 200,000 weeds per hour.
Carbon Robotics’
Autonomous LaserWeeder also uses computer vision techniques, in conjunction with GPS location data, for autonomous navigation. In addition to the weed detection model, this autonomous agent is also equipped with a furrow detection model, which allows it to distinguish the trail it is supposed to follow from plant beds. The company was a sponsor for ICCV in 2021.
OneSoil
Founded in 2017, Zurich-based
OneSoil employs computer vision techniques on satellite imagery to help farmers maximize their yields and reduce costs. In 2018, OneSoil released the
OneSoil Map - a comprehensive map of farmland across 59 countries. To generate this map, they used 250 Tb of satellite imagery shot by the European Union’s Sentinel-2 satellite. Using proprietary computer vision models, they detected clouds, shadows, and snow, and removed these to generate clean images. To combat the low-resolution of satellite images, they combined images taken over a multi-year span.
The free OneSoil App, which was crowned the 2018 Product Hunt AI & Machine Learning Product of the Year, allows farmers to zoom in and select their plot of land without the need to delineate the boundaries themselves. The key to this feature is OneSoil’s field boundary detection model. To train the model, they worked with a number of farmers to get small samples of field boundary data, and used data augmentation operations to increase the size of their training dataset by multiple orders of magnitude.
Given the ease of use, it’s no wonder that more than 300,000 farmers use the app, representing around 5% of the world’s arable land. As of 2023, OneSoil’s computer vision models can identify 12 major crop types, and the company uses this information to generate
productivity zone and soil brightness maps which help farmers make better use of their land.
Taranis
Located in Westfield, Indiana,
Taranis has been around for almost a decade and raised more than $100 million to bring farmers leaf-level insights making it a key player in the future of computer vision in agriculture. The Taranis team has collected and digitized a dataset consisting of over 50 million submillimeter, high-resolution images, and over 200 million data points. These images are used to train custom computer vision detection algorithms for weeds, diseased plants, insects, and nutrient deficiencies.
Taranis’s
AcreForward Intelligence uses real-time agricultural imagery from multiple sources, such as drones, planes, and satellites, to identify insect damage on a per-leaf basis, detect weeds before they become a problem, find nutrient deficiencies, and count the number of plants in a field so farmers can make informed decisions about planting and usage of inputs. In 2022, one of the world’s largest venture capital firms, Andreessen Horowitz, named Taranis one of the top 50 companies kickstarting the American renewal.
Blue River Technology
A subsidiary of John Deere,
Blue River Tech was acquired by the agriculture powerhouse in 2017 for a cool $305 million. When the company started, they narrowed in on lettuce farming, using computer vision and machine learning models to help space plants for maximal yield. Since these relatively humble beginnings, Blue River Tech’s computer vision capabilities have expanded to include sensor fusion, object detection, and segmentation. All of these techniques come together in their
See & Spray technology.
In traditional broadcast spraying, chemicals are sprayed uniformly over an entire field. This practice leads to wasted herbicide, which is costly to the farmer, can pollute the environment, and can foster resistance to the applied chemicals. See & Spray uses object detection to identify weeds in real time so that herbicide can be applied only where it is needed, resulting in 77% reduction in herbicide. Blue River Tech takes weed detection further by housing its See & Spray technology in an autonomous tractor equipped with 6 stereo cameras.
Together, these cameras allow for an on-board computer to estimate depth information for objects surrounding the tractor using sensor fusion. Color (RGB) data and depth information are fed into a semantic segmentation model, which divides the world into five categories: drivable terrain, sky, trees, large objects such as people, animals, and buildings, and the implement being used by the tractor. The autonomous farm tractor equipment stops whenever a large object is detected in its path, and is trained to err on the side of caution by weighing false negative large object detections more strongly than false positives. When the tractor stops, the images are sent to the cloud to be reviewed by humans. Blue River Tech’s specialized agriculture computer vision models were trained on a growing dataset which already contains more than one million images.
Voxel51
Founded in 2018 and headquartered in Ann Arbor, Michigan,
Voxel51 is a computer vision software company empowering researchers, engineers, and organizations to build better AI models through data-centric development. The company’s platform, FiftyOne, has become a go-to tool for visualizing, curating, and evaluating large-scale agricultural image and video datasets—capabilities that are critical in advancing computer vision in agriculture.
FiftyOne enables agritech teams to explore high-resolution agricultural imaging from drones, satellites, and autonomous farm equipment, quickly surface labeling errors, and analyze model performance across key tasks such as crop disease detection, weed segmentation, and yield estimation. By giving users powerful visual insight into how their data and models behave, Voxel51 helps accelerate innovation in agriculture computer vision—from precision farming to autonomous farm equipment.
Used by Fortune 500 companies and leading AI labs, Voxel51 continues to drive the development of transparent, explainable, and production-ready computer vision systems for agriculture that feed, fuel, and sustain the world.
Computer vision in agriculture: Datasets and competitions
If you are interested in exploring applications of computer vision in agriculture, here are some datasets and competitions to get you started:
The next generation of computer vision in agriculture
The role of computer vision in agriculture is expanding rapidly—helping farmers address critical challenges in productivity, sustainability, and labor efficiency. From agricultural imaging for crop health to autonomous farm equipment powered by AI, these technologies are reshaping how we grow and manage food.
Platforms like Voxel51’s FiftyOne are playing a pivotal role in this transformation by enabling researchers and developers to efficiently build, evaluate, and optimize computer vision models tailored to agricultural needs. By improving
dataset curation, model performance, and interpretability, such tools accelerate the deployment of real-world computer vision solutions that can make farming smarter, more adaptive, and more equitable.
Ultimately, as computer vision continues to mature, it will become a cornerstone of modern agriculture—bridging technology and nature to ensure a more sustainable and food-secure future.
