What is computer vision for drones and aerial imagery?

Computer vision for drones uses AI models to automatically detect, classify, segment, and track objects in images and video captured from aerial platforms. Ultralytics YOLO processes this imagery to identify crops, animals, infrastructure, and environmental features without manual review.

Can Ultralytics YOLO run directly on a drone?

Yes. YOLO models can be deployed on edge devices and onboard drone hardware for real-time, in-flight analysis, or run imagery through on-premise and cloud infrastructure for post-flight processing of high-resolution data.

How is YOLO used in precision agriculture?

YOLO models detect crops and weeds for targeted spraying, monitor plant health and disease signs, and support fruit detection, counting, and classification for yield estimation and harvest planning across large fields.

Can computer vision detect and count animals from drone footage?

Yes. YOLO can detect, classify, count, and track livestock, fish, and wildlife from aerial imagery, supporting farm monitoring, aquaculture operations, and conservation surveys across large or hard-to-reach areas.

What industries use aerial computer vision?

Common industries include agriculture, aquaculture, environmental monitoring, and land conservation, alongside infrastructure inspection, security and surveillance, defense, and satellite and geospatial analysis, where aerial platforms capture imagery for scalable, automated monitoring.

What computer vision tasks does YOLO support for aerial imagery?

Ultralytics YOLO supports object detection, oriented bounding boxes, image segmentation, classification, pose estimation, depth estimation, and object tracking across both still images and video captured from drones and aerial devices. Oriented bounding boxes matter most for aerial work, because objects seen from above sit at any angle, so aircraft, ships, vehicles, and buildings are detected at their true orientation rather than boxed off square.

Computer vision for aerial imagery

Transform drone and aerial imagery into real-time insights for agriculture, aquaculture, environmental monitoring, conservation, and geospatial analysis with Ultralytics YOLO.

Trusted by the world's leading organizations

How Ultralytics YOLO tackles aerial imagery

See more from above with vision AI

Ultralytics YOLO helps organizations detect, classify, segment, and monitor objects captured by drones and other aerial platforms. Build accurate models and deploy them in the cloud, on edge devices, or directly within autonomous systems.

Plug-and-play deployment: Deploys with minimal overhead, cutting integration time to days.
Detection accuracy: State-of-the-art real-time detection across objects, environments, and dynamic scenes.
Sub-5ms inference: Edge, cloud, or on-premise deployment with 22 export formats.
Production-ready in hours: Annotate, train, and deploy reducing time-to-market.

License YOLO

Measured results from aerial vision workflows

Peer-reviewed aerial-imagery studies using Ultralytics YOLO report measured results across precision spraying, forest health, and aerial asset inspection.

Precision spraying70%

Spray only where the weeds are

UAV weed maps built with Ultralytics YOLO8 showed over 70% of maize and tomato fields were weed free, so site specific spraying can skip them, with 97% weighted F1 on nine weed species and 0.93 mAP on detection.

Mesías-Ruiz et al., Precision Agriculture, 2026

Forest health94.2%

Catch tree disease before it spreads

Pine wilt disease detection from UAV remote sensing imagery reached 94.2% precision and 88.9% mAP on a YOLO11 based detector, including the early stage infected trees that ground surveys tend to miss.

Shi et al., Ecology and Evolution, 2025

Aerial asset inspection95.7%

Field validated at network scale

Autonomous drone missions with Ultralytics YOLO11 reached 95.7% mAP50 across seven component and defect classes while inspecting 2,925 support structures over 231 km of power lines in five Brazilian states.

Martins et al., Sensors, 2026

Try YOLO26 inference

Drag and drop an image to see real-time object detection

Input

Upload an image or select an example to test Crop and Weed Segmentation YOLO26n. Predict Docs

Live Demo

Drop an image here

or click to browse

Supports JPEG, PNG, WebP, AVIF, HEIC, JP2, TIFF, BMP & more (max 10MB)

Or try an example

Confidence

0.25

IoU

0.70

Image Size

640

Prediction results will appear here.

Deploy your model to get a dedicated inference endpoint with API key authentication — then replace the placeholder URL and key below with your deployment values. Deploy docs

import requests

# Deployment URL and API key
url = "https://your-deployment-url.run.app/predict"
api_key = "YOUR_API_KEY"

# Optional inference parameters (conf, iou, imgsz)
args = {"conf": 0.25, "iou": 0.7, "imgsz": 640}

with open("image.jpg", "rb") as f:
    response = requests.post(
        url,
        headers={"Authorization": f"Bearer {api_key}"},
        data=args,
        files={"file": f},
    )

print(response.json())

Turn aerial imagery into actionable intelligence

Purpose-built solutions for every stage of your production process.

Crop detection and precision spraying

Spray smarter with Ultralytics YOLO

Leverage Ultralytics YOLO to identify crops and weeds in real time, enabling targeted fertilizer application that reduces chemical use and protects healthy plants.

Weed identification: Detect weeds from crops across varying field conditions.
Targeted application: Guide precision spraying to exact plant locations, not entire fields.
Reduced chemical use: Cut herbicide costs and environmental impact with selective spraying.