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https://github.com/sisasima/ecological-automation-of-plant-care
Shared files for project EAoPC
https://github.com/sisasima/ecological-automation-of-plant-care
circuitpython ecology electronics
Last synced: about 1 month ago
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Shared files for project EAoPC
- Host: GitHub
- URL: https://github.com/sisasima/ecological-automation-of-plant-care
- Owner: SisaSima
- Created: 2024-09-05T14:58:08.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-16T16:03:28.000Z (about 2 months ago)
- Last Synced: 2024-09-22T14:32:25.639Z (about 2 months ago)
- Topics: circuitpython, ecology, electronics
- Language: Python
- Homepage:
- Size: 653 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Ecological-Automation-of-Plant-Care
We are currently facing many environmental challenges, such as global warming and water scarcity, which pose great challenges for effective sustainable plant cultivation. Traditional methods of irrigation and care often lead to insufficient watering or, conversely, to overflowing the plants, which not only wastes water, but can also cause soil rot, which can later appear on the products.
In the future, an innovative solution to these problems may be ecological automation of plant care, which combines automated irrigation based on machine learning and recycling of excess water.
The main goal of the work is to create an automated system that monitors and controls soil irrigation, while responding to environmental factors such as air humidity and ambient temperature. An important part of the project is also efficient water management through its recycling. Excess water is captured, filtered and reused, which significantly contributes to reducing consumption and promotes sustainability.
This work describes in detail the individual steps of the development of this device from the design to the implementation of sensors and machine learning algorithms.
# Hardware
Circut diagram and PCB were made in KiCAD.
Used electroparts:
- Raspberry pi Pico W - https://techfun.sk/produkt/raspberry-pi-pico-w/
- SHT30 - https://techfun.sk/produkt/senzor-teploty-a-vlhkosti-vzduchu-sht30/
- Capacitive Soil Humidity sensor - https://techfun.sk/produkt/kapacitny-senzor-vlhkosti-pody/
- OLED 1.3" display 128x64 - https://techfun.sk/produkt/oled-1-3-display-128x64/
- Water Level sensor (homemade)
- Small Water Pump - https://techfun.sk/produkt/mala-vodna-pumpa/
- Battery 5V
- Charging Module - https://techfun.sk/produkt/nabijaci-modul-pre-litiove-baterie-tp4056-ochranny-obvod-rozne-typy/
### Diagram
### PCB
# Code
To run the code on the Raspberry pi Pico W, you need to install circuitpython on it. Detailed instructions for its installation can be found [here](https://learn.adafruit.com/welcome-to-circuitpython/installing-circuitpython).
Then you just transfer the files from the code folder to the device. In order for the programs to run, it is necessary to move the "lib" folder, in which all used libraries are stored, to the device.
To immediately, automatically start the program after starting, it is necessary to name the required file "code.py" (this step is already done). IN the folder there are wro versions of "code.py" file:
- code.py
- code_v_noML.py
"code_v_noML.py" has no Machine Learning algorithm implemented and it's used just for testing functionnality od circuit and program. On the other hand code.py has already implemented algorithms and is used as a final version of code.
### Machine Learning?
As I mentioned, this project is powered by machine learning algorithm used to find the perfect amout of water to irrigate the soil. This operation is done by calculating difference between wanted soil moisture and the one we got by irrigating. the difference is than used to enlenght or enshort the time of irrigation.
```python
doba_polievania = doba_polievania*(chcena_val/(moisture_level*(120/100)))
```
The resulting value is then saved to a file in the SD card and then used in the next cycle as the watering time. The same way is stored the value of soil moisture after watering, so that it is then possible to create a graph comparing these two values
### Dataread
In order to better navigate the collected data, I programmed the code that creates a graph from it.
You can find the program and its user manual in the "dataread" folder.
The result should look like this:
# 3D Model
I shared a 'box_model.3mf' file, ready to be printed on 3d printer. The model should look like this:
There is a place left for the battery and charging module. I personally filled the hole with memory foam and powered it directly with micro USB-B (connected to Raspberry pi Pico W).
#### WARNING! UPPER HOLE FOR USB CABLE is too small. You can enlarge it in any 3D modeling program. In case you are not able to do that, you can use a drill.
