#Herd cattle with drones?
In the recent documentary series Clarkson's Farm (2021), Jeremy Clarkson took us on his journey and showed us how he herded sheep with a barking drone successfully - or maybe not 🤣.
Over the last few years, drone-based smart agriculture solutions have become increasingly popular due to numerous benefits, including deeper insights into farming assets and data-driven decision-making process for better efficiency and reduced operational cost.
To explain the solutions on a high level, it usually includes the following processes:
- Deploy drones and sensors to collect livestock data
- Use AI technology to process and analyse the data for locating, counting or monitoring cattle
- Provide easy-to-understand insights and takeaways to enable ranchers to make data-driven decisions or take immediate actions when issues arise
To showcase the above processes, read on or watch our video to see how to build an object detection ETL pipeline using VDP to analyse a drone video of a cattle farm, and create a Cow Counter dashboard with Metabase that provides insights about cattle in the video footage.
Where there are discrepancies between tutorial and video, please follow the tutorial.
- Docker and Docker compose
- Python 3.8+ with an environment-management tool such Conda
#Build an object detection ETL data pipeline
In the Build a shareable object detection application with VDP and Streamlit tutorial, we built a
SYNC object detection pipeline that responds to HTTP requests synchronously with VDP. This time, we are going to use the same model YOLOv7 to build an object detection pipeline but in
ASYNC mode. Unlike a
SYNC pipeline, this pipeline
- accepts HTTP requests with image payload,
- transforms the images in the payload into structured insights, and
- sends the structured insights into a PostgreSQL database.
#Run VDP locally
git clone https://github.com/instill-ai/vdp.git && cd vdpmake all
Once all the services are up, go to the Console at http://localhost:3000.
#Build an ASYNC data pipeline with YOLOv7
After onboarding, you will be redirected to the Pipeline page. Please follow the steps of Build an ASYNC detection pipeline.
add a HTTP source,
import a model from GitHub repository instill-ai/model-yolov7 with ID
deploy a model instance
v1.0-cpuof the imported model,
add a PostgreSQL destination, with ID
set up a pipeline with ID
Swipe to see other images.
Alternatively, you could refer to the low-code example to build this pipeline programmatically via REST API.
Now you should see the pipeline
detection in the Console.
#Trigger the pipeline to analyse a drone video
#Create a virtual environment and install dependencies
We'll use Conda as the package management system like we did it in the last tutorial.
First, create and activate an environment named
vdp-dashboard with Python 3.8:
conda create --name vdp-streamlit python=3.8conda activate vdp-dashboard
Then, go to /examples/async/yolov7 directory of the VDP project to install all dependencies.
cd examples/async/yolov7pip install -r requirements.txt
#Trigger the pipeline
The directory of the project should look like the following
├── Dockerfile-metabase-m1├── README.md├── main.py├── requirements.txt└── utils.py
To run the demo for processing the video, we simply do
You could skip to the next section to create the dashboard. But if you're interested, here we dive into the demo details.
First, the script will download a 1-minute drone video of a cattle farm
cows_dornick.mp4 from a public Google storage bucket.
The video used in the demo is sampled from a public video
cows dornick.mp4 from here.
After finishing downloading the video,
extract_frames_from_video function will extract frames from the video into a folder
inputs at 30 frames per second.
Then, we trigger the pipeline
detection to analyse the video by sending HTTP requests to process all frames in the
Each triggering pipeline operation will send the analysed result to the configured PostgreSQL database and return a list of unique indexes corresponding to all processed image frames in the payload.
It may take a while to analyse the full video. Meanwhile, we can connect to the Postgres database to check whether it is working as expected. You should see the tutorial database is already populated with some data 🚀.
In the next section, we will setup Metabase to connect to the data in the destination PostgreSQL database and build a dashboard based on the analysis results.
#Create a Cow Counter dashboard
Metabase is an open-source business intelligence (BI) tool that helps you uncover the data insights like an analyst. Just connect to your database, you can dig deeper into your data, easily build interactive dashboards and share them with stakeholders.
#Run Metabase locally
docker pull metabase/metabase:latestdocker run -d -p 3100:3000 --name metabase metabase/metabase
Note: If the official Metabase docker image doesn't work on M1 (apple silicon) mac, try to build an image with the
docker build -f Dockerfile-metabase-m1 -t metabase/metabase-m1 .docker run -d -p 3100:3000 --name metabase metabase/metabase-m1
Once the Metabase startup completes, you can access it at http://localhost:3100.
#Create the dashboard
During onboarding, add your PostgreSQL database.
Then, click Browse data on the left sidebar, choose vdp-postgres ➝ Airbyte Raw Vdp:
As you can see, the pipeline outputs are stored in the
Airbyte Data ➝ Detection ➝ Objects field with JSON blob format.
#Convert raw detections into multiple records
To flatten the raw detections (JSON blob) into multiple records for further analysis, Click +New on the top right corner, choose SQL query ➝ Select a database vdp-postgres, then copy and paste the following SQL query and press Run query:
The transformed data counts every time a cow appeared in the video footage.
#Visualise the data
Click on Visualization to open the visualization sidebar and choose Area. For Data tab, select the fields
category for X-axis, and select the field
Count for Y-axis. You can customise the area chart as you want. Here we just enabled Show values on data points in the Display tab.
Metabase has a hard limitation 2,000 to the number of rows displayed for a table. Therefore, the dashboard won't show all the detections.
To make the dashboard accessible, you can save this question into the Our analytics collection by clicking Save on the top right and give it a name cow counter.
🎉 Congrats! You've built a simple yet intuitive dashboard to count cows in a drone video footage. To validate the dashboard, the demo script
main.py also generates a video
output.mp4 with detections drawn on frames by the end of the demo.
We use YOLOv7 in the ETL pipeline, since it is the state-of-the-art object detector that surpasses all known ones in both speed and accuracy. The above video showed that it worked fairly well considering it was only trained on the public MS COCO dataset without any fine-tuning.
However, if you want to improve the performance, as you can spot that some cows were wrongly detected as "dogs" on a few frames, it is highly recommended to fine-tune the model on labelled drone data collected from the same domain.
We are working closely with our early users to deploy customised models for their use cases. If you're interested, please fill out the form to tell us about yourself and your project, we will be in touch 👐.
In this tutorial, you've built an ASYNC object detection pipeline using VDP to process a video and sent the analysis result to a Postgres database. Also by using Metabase, you've turned the analysis result into an intuitive and interactive dashboard that you can share with stakeholders.
If you enjoyed VDP, we're building a fully managed service for VDP - Instill Cloud (Alpha):
- Painless setup
- Maintenance-free infrastructure
- Start for free, pay as you grow
We also invite you to join our Discord community to share your use cases and showcase your work with Data/AI practitioners.