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Distributed Computer Vision Software & Raspberry Pis to help manage a farm
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yin-yang-ranch: Software & Raspberry Pis help manage a farm


This repository is a collection of Python programs and Raspberry Pi hardware projects to help manage a small urban permaculture farm called Yin Yang Ranch. The 2 acre farm is an ongoing science project to build living soil, capture rain in barrels, and grow a variety of plants and fruit trees that can feed birds, bees, butterflies and people. We are in Southern California about 10 miles from the Malibu coast. Drought and limited rainfall are the toughest climate issues. Monitoring and observation are important, so I built a Raspberry Pi Camera system to read the water meter and monitor temperatures to optimize irrigation. I can send a text message to the system ("Susan") to ask about water usage or temperatures:


This repository contains the software and the hardware designs used to build our measurement and monitoring systems. yin-yang-ranch is a continuously evolving project with a lot of hardware hacking and software refactoring. I am open-sourcing everything in case it might be helpful to others. My projects use Raspberry Pi computers, PiCameras, various sensors and related electronics. I control the hardware with Python programs that use computer vision, OpenCV, Numpy, pandas, and the PyZMQ messaging library. I use the Raspberry Pi GPIO Python module to control lights (e.g., to light the water meter) and irrigation valves.

I currently have 4 repositories on GitHub:

  1. yin-yang-ranch: this repository. Overall project design and goals.
  2. imagezmq: Transporting OpenCV images.
  3. imagenode: Capture and Send Images and Sensor Data.
  4. imagehub: Receive and Store Images and Event Logs.

imagezmq moves images taken by Raspberry Pi computers to hub computers for image processing. imagenode runs on multiple RPi computers, continuously capturing images, detecting motion, and gathering sensor data (e.g. air and soil temperatures). imagehub runs on a Mac or a Linux computer and receives images and event messages from 8-10 Raspberry Pi computers simultaneously. I use a variety of computer vision techniques implemented in Python. I have programs that can read the water meter. Or tell if that critter moving behind the barn is a coyote or a racoon.

I also have a website at that will display some dashboards on weather, compost temperatures, solar power generation and when the last coyote was spotted. It is mostly a few pictures of the ranch for now while I am developing the dashboard software.

The Overall Design

The overall system design is a hub and spoke network with ZMQ messaging between Raspberry PiCameras and imagehubs. One image hub can simultaneously receive images from about 10 PiCameras. A librarian program stores images and extracted image features in a database. A communications program uses the database to answer queries about images and events, as shown in the SMS text exchange pictured above. By distributing computer vision processing pipelines across Raspberry Pi computers and more powerful computers like Macs, each computer can do what it is best at. A Raspberry Pi can take pictures with the PiCamera and adjust camera settings, control additional lighting, crop, flip and grayscale images, as well as detect motion. A Mac can store and index images from many Raspberry Pi computers simultaneously. It can perform more complex image processing like reading the changing digits of the water meter or using image classification techniques to label a coyote or a raccoon in an image stream. My current setup has about a dozen Raspberry Pis with PiCamera modules and 2 linux laptops with webcams attached to a single imagehub.


The project contains code repositories for each part of the design shown above (The first 3 have been pushed to GitHub so far):

  • imagenode: image capture on Raspberry Pi and other computers using PiCameras, webcams and various OpenCV techniques for image rotation, threshholding, dilation, differencing and motion detection. See imagenode: Capture and Send Images and Sensor Data.
  • imagezmq: Python classes that transport OpenCV images from imagenodes to imagehubs. See imagezmq: Transporting OpenCV images.
  • imagehub: Python programs that gather images and sensor data from multiple Raspberry Pi and other computers via imagezmq. See imagehub: Receiving and saving images and event data from multiple Raspberry Pi's.
  • librarian: Python programs that index and store images, as well as perform additional image processing including feature extraction, image and object classification and creating text descriptions and summaries.
  • commhub: Python programs that provide a natural language interface for asking various questions about the images (is the water running? was a coyote sighted today?) using data compiled by the librarian.
  • commagents: Python programs that connect various communication channels to the commhub, including an SMS/texting agent (example shown above), an email agent, a webchat agent and an agent to keep the Yin Yang Ranch dashboard updated.
  • yin-yang-ranch (this GitHub repository): Overall project documentation and design. Also contains Python programs that manage operations, like monitoring the health status of all the subsystems, including electrical power and internet access. Currently, this repository is mostly documentation for the overall system and some hardware descriptions and diagrams.

This distributed design allows each computer to do what it does best. A Raspberry Pi with a PiCamera can watch a water meter for needle motion, then transmit only those images show the water flow changes (from flowing to not flowing or vice versa). The logic for motion detection and image selection runs in the Raspberry Pi, which only sends relevant images to the imagehub, saving network bandwidth. The imagehub stores the event messages and images from multiple nodes at the same time. The librarian program does further analysis of the images and event messages, for example, using character extraction and recognition to read the numeric digits in the water meter and answer questions about water flow per day or per month. A more complete "which computer does what" explanation can be found in Distributing tasks among the multiple computers.

Software Stack for the entire system

The system is written in Python and uses these packages. Higher versions will usually work fine, but these specific ones are known to work. See each specific repository above for more software details.

  • Python 3.5 and 3.6
  • OpenCV 3.3
  • Raspian Stretch
  • PyZMQ 16.0
  • imutils 0.4.3 (used get to images from PiCamera)

Hardware and Electronics

The project uses a wide variety of electronics hardware:

  • Raspberry Pi computers with both PiCameras and webcams.
  • Mac and Linux laptops (some with webcams as nodes).
  • Temperature and humidity sensors.
  • Lighting control electronics (e.g., to light the water meter).
  • Motion detection sensors (both PIR and ultrasonic).
  • Infrared lighting arrays (to watch for coyotes and raccoons at night).
  • Irrigation actuators to turn water on and off.
  • Solar panel monitoring hardware with programs to optimize power use and track the daily, monthly and annual sunshine energy reaching the farm. Hours and intensity of sunlight are big factors in photosynthesis, plant growth rates and water requirements.

Water Meter Hardware Example

This is what a water meter looks like:


The water meter project uses computer vision to manage water use on the farm. I can use computer vision to determine if water is flowing or not, read the gallons used per hour or per day, and save some of the images for analysis. The project also watches for unusual water flow due to leaks or broken irrigation controls and sends alerts. When the water is flowing, the large analog needle spins clockwise. Each full rotation of the needle causes the rightmost digit of the digital meter to advance by one digit. The small "blue star" dial is a "leak detector" that spins even when a very small amount of water is flowing (like a dripping faucet).

The Raspberry Pi sits in a mason jar on top of the water meter cover. The PiCamera and the array of LED lights is underneath the water meter cover and aimed at the water meter face. Here is a picture of the water meter as seen by the PiCamera:


For more details on the water meter camera electronics and buildout, see Water Meter Camera Hardware Details.

Coyote Cam and Temperature Sensor Hardware Example

Raspberry Pi nodes around the farm can monitor temperature and detect motion of critters wandering about. Here is a log that shows motion detected behind the barn, along with a couple of pictures that were taken when the coyote activated the motion detection in the imagenode RPi running in the barn:


Here is what the back of the barn looks like with the infrared "PiNoir" style PiCamera, a temperature sensor and the infrared floodlight that lights the area after dark without putting out white light:


For more details on the infrared camera, infrared floodlight and temperature sensor, see Critter Infrared Camera and Temperature Sensor Details.

Driveway Cam Raspberry Pi Zero Hardware Example

Another PiCamera imagenode watches the driveway and entrance area. It sees the mail truck come and go, and spots an occasional hawk. It uses a Raspberry Pi Zero W computer and a PiCamera that are encased in a "fake security camera" housing that cost about $5:


And here is what it looks like assembled and mounted in our driveway. You can see the PiCamera behind the housing lens:


For more details on the Pi Zero based driveway camera and its enclosure, including the assembly pictures and some "action shots", see Driveway Camera Hardware Example.

Research and Development Roadmap

The yin-yang-ranch projects are in early development and testing. Prototypes for all the modules in the design diagram above are working, and the early experiments have provided a lot of data to help with design changes and code refactoring. I have pushed the imagezmq, imagenode and imagehub repositories to GitHub (see links above).

The librarian and communications programs will follow in early 2019. Hardware designs, diagrams and how-tos will be posted to this yin-yang-ranch repository over the spring and summer of 2019.

There are many styles and choices about "when to push to GitHub" and when to share a project with the open source community. I am choosing to share my projects early in the development cycle, which means there is no code in this repository yet. My style is to write design and documentation first, then prototype the code and then iterate. So my first drafts and beta repositories contain documentation and design and TODO scaffolding before they contain code. I push them in these early stages to share them with collaborators (and with friends and relatives who wonder what IS that guy doing in retirement?).

The imagezmq repository contains test programs that show how images can be sent from multiple Raspberry Pi computers simultaneously to a hub computer. The imagenode and imagehub programs are evolutions of and The Python code in those two programs is a brief "pseudo code" outline for the code that is in the imagenode and imagehub programs. Links to the full imagenode and imagehub repositories are above.


The yin-yang-ranch projects are in very early development and testing. I welcome questions and comments. The easiest way to make a comment or ask a question about the project is to open an issue.

Acknowledgments and Thank Yous

  • The Raspberry Pi Foundation and their remarkable Raspberry Pi tiny single board computers. Even their $10 Pi Zero runs Linux and OpenCV and can do serious computer vision image acquisition and processing. Raspberry Pi Foundation
  • Adafruit an amazing resource for electronics makers with helpful tutorials and electronic components of all kinds. Adafruit
  • ZeroMQ is a great network messaging library with great documentation at
  • OpenCV and its Python bindings provide great scaffolding for computer vision projects large or small:
  • is the best resource for installing OpenCV and its Python bindings. Adrian Rosebrock provides many practical OpenCV techniques with tutorials, code examples, blogs and books at Installing OpenCV on my Raspberry Pi computers, Macs and Linux boxes went from frustrating to easy thanks to his tutorials. I also learned a LOT about computer vision methods and techniques by taking his PyImageSearch Gurus course. Highly recommended.
  • imutils is a collection of Python classes and methods that allows computer vision programs using OpenCV to be cleaner and more compact. It has a very helpful threaded image reader for Raspberry PiCamera modules or webcams. It allowed me to shorten my camera reading programs on the Raspberry Pi by half: imutils on GitHub. imutils is an open source project authored by Adrian Rosebrock.
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