10 Computer Vision Agriculture Use Cases & Examples ['25]

Labor shortages, resource inefficiencies, and environmental pressures increasingly challenge agriculture. Climate change adds to this burden through extreme weather, water scarcity, and rising pest threats, further straining productivity and sustainability.¹

Computer vision offers targeted solutions by enabling automation and data-driven insights across critical farming operations. Applications range from crop monitoring and sorting to disease detection and livestock management.

Explore the top 10 computer vision agriculture use cases helping the industry address current problems and drive future growth.

Computer vision agriculture use cases

1. Crop monitoring with drones

Drones equipped with cameras and sensors help monitor crops efficiently. Using computer vision techniques, these drones detect early signs of stress, pest attacks, and variations in soil moisture. They support image acquisition at scale, reducing the need for time-consuming manual labor.

In precision agriculture, drone-based crop monitoring enables farmers to assess crop health and growth and map field conditions.

Real-life example:

Garuda Aerospace, an Indian drone manufacturer, offers agricultural drones for crop monitoring.

Challenge and solution:

Farmers faced challenges manually monitoring large fields for crop health and soil conditions. By deploying Garuda’s drones equipped with computer vision technology, they could efficiently monitor vast farmlands, detect abnormalities, and make informed decisions.²

2. Crop sorting and grading

Sorting and grading harvested produce is a repetitive and time-intensive process. Through image analysis, computer vision models automate these tasks by detecting size, shape, color, and surface defects.

These agriculture computer vision systems can sort produce like potatoes or apples based on order requirements.

Real-life example:

TOMRA Food, a company specializing in sorting solutions, introduced AI-powered sorting machines like the TOMRA 5A (see the image below).

Challenge and solution:

Producers experienced inefficiencies in sorting large volumes of produce for quality control. By implementing TOMRA’s computer vision technology, they achieved high accuracy in detecting defects and foreign materials, enhancing product quality, and reducing waste.³

Figure 1: TOMRA 5A sorting machines.

3. Pesticide spraying with drones

Pesticide spraying can be hazardous and time-consuming. Drones with spray systems and computer vision applications identify affected areas and apply pesticides precisely.

These drones perform advanced image recognition and analysis to detect abnormal crop conditions and trigger spraying only where needed.

Real-life example:

DJI, a drone technology company, developed the Agras T40 drone for agricultural spraying.

Challenge and solution:

Farmers struggled with uneven pesticide application and labor-intensive processes. By utilizing the Agras T40’s computer vision and AI capabilities, they achieved uniform pesticide distribution, reduced chemical usage, and improved crop health.⁴

Figure 2: Agras T40 drone example for agricultural spraying.

4. Computer vision phenotyping

Phenotyping studies plant traits like shape, size, and color. Traditionally done by hand, it is now accelerated using artificial intelligence and computer vision tasks.

Cameras capture detailed images of plants, which deep learning models analyze to extract features and track growth stages.

Real-life example:

PhenoRob, a research initiative by the University of Bonn, focuses on robotics and phenotyping for sustainable crop production.

Challenge and solution:

Researchers needed efficient methods to analyze plant traits across large fields. By deploying computer vision and robotics, they could non-invasively monitor plant development, aiding in breeding and crop management decisions.⁵

5. Livestock farming

Computer vision in agriculture also applies to livestock management. High-definition cameras and deep learning algorithms monitor animals’ health, feeding behavior, and movement patterns.

These computer vision techniques detect abnormal behavior like limping or reduced activity.

Real-life example:

Connecterra, a Dutch company, developed the Intelligent Dairy Farmer’s Assistant (Ida) platform.

Challenge and solution:

Dairy farmers faced difficulties in continuously monitoring cow health and behavior. By using Ida’s AI-driven insights from sensor data, farmers could proactively address health issues, improving animal welfare and productivity.⁶

6. Weed detection and removal

Weeds lower crop yield by competing for water, nutrients, and sunlight. Traditional farming practices rely on manual weeding or herbicides. Both are time-consuming, and chemical use raises environmental and health concerns.

Using advanced image recognition and object detection, they distinguish between crops and weeds in real-time. Automated weeders equipped with these systems can mechanically remove weeds without harming nearby crops.

Real-life example:

FarmWise Labs, a U.S.-based agtech company, has developed the Titan FT-35, an automated mechanical weeder.

Challenge and solution:

Due to labor-intensive and chemical-reliant weed control methods, farmers needed a comprehensive solution to address these problems. By employing computer vision and robotics, the Titan FT-35 identifies and removes weeds without chemicals, enhancing efficiency and sustainability.⁷

7. Soil health assessment

Modern soil health assessment combines remote sensing, IoT networks, and advanced analytics to provide accurate, real-time insights for precision agriculture.

Multispectral and hyperspectral imaging from satellites and drones is used to estimate parameters such as moisture, organic matter, salinity, compaction, and nutrient levels across large fields with up to 90 percent accuracy.

Integrated platforms combine remote sensing, sensor inputs, lab data, weather, and yield history to create comprehensive soil health indices using AI models. Transformer-based data fusion methods have further improved predictive performance for key soil properties.

Robotic sampling systems add automation by collecting and analyzing soil in the field, eliminating the need for manual lab testing. Analytics tools like those from CropX visualize this data through mobile dashboards, delivering field maps, variable-rate recommendations, risk alerts, and carbon tracking to support sustainable and climate-resilient farming practices.

Real-life example: Farmonaut’s Integrated Soil Health Platform

Farmonaut provides a comprehensive soil health monitoring solution by combining satellite imagery with in-field sensor data. Its system utilizes multispectral and hyperspectral satellite imaging to assess parameters such as soil organic carbon, moisture, salinity, and nutrient distribution across large field areas.

These remote sensing insights are complemented by real-time data from IoT sensors and portable soil testing kits, which measure pH, electrical conductivity, and biological activity with high accuracy. Through machine learning algorithms, Farmonaut integrates these datasets to generate actionable recommendations for irrigation, fertilization, and erosion management.⁸

Real-life example: AgroLens’ Transformer-Based Nutrient Prediction

AgroLens is a machine learning framework that predicts soil nutrient levels using satellite imagery and auxiliary agricultural data. It leverages data from Sentinel-2 satellites along with regional weather, crop yield history, and soil characteristics to train predictive models such as random forest and gradient boosting.

Recent developments have incorporated transformer-based data fusion techniques, significantly enhancing the accuracy of soil nutrient and pH estimation. This approach is particularly beneficial in regions with limited access to laboratory infrastructure, offering scalable and efficient soil diagnostics to support precision agriculture practices.⁹

8. Aquaculture monitoring

Overfeeding and disease outbreaks can reduce yield and affect animal welfare in aquaculture. Manual monitoring of fish or shrimp behavior is often limited and lacks precision.

Computer vision systems analyze visual and audio data from underwater or surface-level cameras. They track animal movement, feeding patterns, and signs of disease, allowing for early detection and better control over farming conditions.

Real-life example:

Bosch Business Innovations applied artificial intelligence and computer vision techniques to shrimp farming.

Challenge and solution:

Farmers were struggling with feed wastage and late diagnosis of shrimp diseases. Bosch developed a system that uses visual data to analyze shrimp behavior and feeding habits. It helped reduce feed costs and improve survival rates.¹⁰

9. Crop ripeness analysis

Harvesting crops at the right time is critical for quality and market value. In traditional farming practices, ripeness is judged visually or by manual sampling, which can be inefficient and subjective.

Computer vision models can track color changes, shape, and texture to estimate ripeness levels. This helps in planning harvest schedules and reduces post-harvest losses. Integrating these models with drone-based monitoring provides a scalable approach.

Real-life example:

A research team developed a framework using vision foundation models for analyzing cranberry ripening stages.

Challenge and solution:

Cranberry growers needed accurate tools to determine harvest readiness across different varieties. The system identified crop maturity using drone imagery and ground-level cameras and helped optimize harvest timing.

Figure 3: Weekly drone imaging was used to inspect cranberry bogs from July to September for four varieties: Mullica Queen, Stevens, Crimson Queen, and Haines.¹¹

10. Edge-AI for disease detection

Identifying plant diseases early can prevent large-scale crop loss. Farmers often lack access to labs or internet-based diagnostic tools in remote or low-resource farming areas.

Edge-AI brings computer vision to portable devices. Lightweight models can run on mobile hardware to analyze plant images and detect early signs of disease. This reduces dependency on cloud systems and enables offline decision-making.

Real-life example:

A recent study introduced an edge-AI approach using YOLOv8-S, a lightweight object detection model designed for use in areas with limited computational resources.

Challenge and solution:

Farmers could upload soil images or leaf photos, and the device would assess crop health and alert them to infections. This approach supported food security and reduced crop loss in under-resourced areas.¹²

Conclusion

Computer vision is becoming an important tool in modern agriculture, offering practical solutions to long-standing challenges. It supports farmers and researchers by providing consistent, data-driven insights into crop health, soil conditions, livestock behavior, and more.

These capabilities help reduce reliance on manual labor, improve the accuracy of field assessments, and enable more targeted use of resources like water, fertilizers, and pesticides.

Rather than replacing traditional farming knowledge, computer vision complements it by making monitoring and decision-making more efficient, especially at larger scales.

As agriculture adapts to climate variability and increasing demand, technologies like computer vision can play a valuable role in improving operational reliability and long-term sustainability.

External Links

• 1. The Future of US Agriculture | The National Environmental Education Foundation (NEEF).
• 2. https://www.garudaaerospace.com/agriculture/crop_management
• 3. TOMRA Food unveils new AI tech - Fruit Growers News.
• 4. DJI Agras T40: The Future of Agricultural Drone Spraying - HobiTech. HobiTech
• 5. PhenoRob - Robotics and Phenotyping for Sustainable Crop Production. PhenoRob
• 6. Connecterra - Intelligent data platform for the dairy industry..
• 7. Bot Verification.
• 8. AI-Powered Soil & Crop Monitoring With Farmonaut. Farmonaut®
• 9. https://arxiv.org/pdf/2410.18353v1
• 10. How we can use AI to create a better society. Financial Times
• 11. https://arxiv.org/pdf/2412.09739
• 12. https://arxiv.org/pdf/2412.18635