PiPlanter | Big Overhaul Update

Okay! So I leave for college in less than 30 days, but I’d like to make sure my tomatoes to continue to grow once I leave so I’ve taken some steps to make sure that my departure goes smoothly.

Here’s a video of my revised setup:

There are a few key differences between this setup and my previous one:

The main one is that the watering system has been 100% re-vamped. The water distribution happens via a hose with holes in it instead of using the tray at the bottom of the plant grid in the previous video.

It also takes, uploads and tweets a picture of itself using a raspberry pi camera module.

It also creates a new mysql table every two weeks, and in turn, renders a new kind of graph. The renderscript.php file receives an argument from the python script which is the table code.

Here’s the python script:

Here’s the .php script:

Thank you for reading!

PiPlanter – A Plant Growth Automator

New Version The Post Below Is Out Of Date Click Here For The New Version


This post is many months in the making and I am very proud of the thing’s I’ve done here, and very thankful to all of those (specifically at www.reddit.com/r/raspberry_pi) who have helped me along my way to getting this project up and running.

This page contains every single post related to this project, please feel free to go back and look at my progression and pick up tips along the way if you want to try something like this.

Let’s get this going, here’s an overview video:

There are 8 parts to this system and, you guessed it, I’ll be going in-depth about every single one!

Sensor Network

So at it’s core, the PiPlanter is a Sensor Network & Pump System. Here’s a video explaining the sensor array:

This project uses a TMP35-37 sensor to get a pretty precise temperature reading of the room. Later down in this post you can find out the algorithm to determine the temperature in Fahrenheit. It also uses a basic LDR to get the relative ambient light level in the room. Along with those two sensors, there are 4 relative humidity sensors of my own design, here’s a picture of them as seen in this post:

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They’re hooked up to the ADC (mentioned later) in the same way that the LDR is, with a voltage dividing resistor, and then fed directly into ADC. The principal behind this sensor is that when you insert it into soil, the water in that soil connected the two probes, causing a voltage to flow across them. So if there is more water in the soil, more electrons will flow across them, and the analog value will be higher. It’s very basic, but it works. I’ve done several long term tests, and over time, as the soil becomes dryer, the value gets lower, indicating relative dryness. Here is a picture of the four probes in the soil, with the plants.

The TMP sensor’s output is plugged directly into the ADC and the LDR is very basically connected to the ADC as well, this is essentially how how the whole thing is setup on the breadboard:

Capture

Pump System

The pump system is pretty dead simple. Essentially it is a PowerSwitch Tail II switching the mains to a 9v DC power supply. The 9v power supply is connected directly to a 12v DC submersible pump. Instead of using a motor driver chip, which requires 3 pins to do, and the chip would get hot and whatnot, I’ve decided to go with this method.

The pump is not self priming. This means it cannot make the transition from pumping air to pumping water. I wrestled with this problem for a long time, and came up with what I think is an elegant solution. I submerged the pump directly into the water, which means the pump will never fill with air, and will always pump water when activated. Here’s a video explaining the pump system:

Raspberry Pi ADC

The next system is the ADC connected to the Raspberry Pi. It is an 8 bit, 8 port analog to digital converter that can easily run on 3.3v so it’s perfect for the pi. Here is the chip, and you set it up as follows (I took this from an earlier post I wrote)

Now we need to set up the specific libraries for python the first of which being spidev, the spi tool for the raspberry pi which we can grab from git using the following commands:

You also need to (copied from http://scruss.com/blog/2013/01/19/the-quite-rubbish-clock/):

As root, edit the kernel module blacklist file:

Comment out the spi-bcm2708 line so it looks like this:

Save the file so that the module will load on future reboots. To enable the module now, enter:

To read from the ADC, add the following to your python code. The full code will be listed later:

So just use “readadc(n)” to get a value.

Python Code

I’ve made a real effort this time to comment my code well, so I’m not going to do a line by line breakdown like I often do, but I will clearly state the installs and setup things as follows. I’m assuming you have python-dev installed.

Download and install: APScheduler, this is a very straight forward install

Download and install: tweepy, you will need to go through the API setup process.

Download and install: flickrapi, you will need to go through the API setup process.

Here’s the source code for the python component of this project:

There you go! Essentially, every hour, the raspberry pi samples data from 4 humidity probes, an LDR and a tmp sensor. Once the sampling is complete, it dumps the data into a mysql database. From there the data is rendered into a graph using pChart in the form of a .png image. From there, that .png files is uploaded to flickr using this api. Once the file is uploaded, it returns it’s photo ID to the python script. From there, a tweet is built containing the brightness at the time of the tweet, the temperature at the time of the tweet, and the average moisture of the plants. It also uses the photo ID from flickr obtained earlier to build a URL leading to that image on flickr which it tweets as well. The final part of the tweet is a url that leads to this post! (taken from)

MySQL Database

The database is extremely simple, after installing MySQL set it up and create table that follows this syntax:

Pretty basic stuff, the table is just where the python script dumps the data every hour.

PChart Graph

The software driving the graphing part of the project is a bit of php graphing software called pchart. It allows me to graph mysql values from a table in a variety of ways. It is very important, and the code for the php script is as follows:

As you may be able to guess, upon the calling of this script, the program looks for a table called “piplanter_table_17” and does a bunch of stuff as commented to produce a graph. This is what a sample graph looks like:

Wed Jun 26 19:39:17 2013

This is data taken over 6 days, and it’s a lot to look at, but it’s good stuff.

Twitter & Flickr Integration

As you hopefully derived from the python code, this project uses Twitter to send data to me. Instead of using an email server or sending sms messages, I decided on twitter because of a few reasons. I use the service constantly, so I won’t ever miss a tweet. The API seemed really easy to use (and it was!) and allowed more than one person to acess the data at any one time. I decided to use flickr as my image hosting service for a lot of the same reasons, but the main one was their 1TB storage per person. You’ve already seen a sample flickr upload, so here’s a sample tweet:

That’s essentially it! Thank you for reading, and please ask questions.

PiPlanter | Bringing most of it together

Last night I finished the majority of the software for this project. Here’s a video of me going over what happened and what the program does in simpler terms:

Essentially, every hour, the raspberry pi samples data from 4 humidity probes, an LDR and a tmp sensor. Once the sampling is complete, it dumps the data into a mysql database. From there the data is rendered into a graph using pChart in the form of a .png image. From there, that .png files is uploaded to flickr using this api. Once the file is uploaded, it returns it’s photo ID to the python script. From there, a tweet is built containing the brightness at the time of the tweet, the temperature at the time of the tweet, and the average moisture of the plants. It also uses the photo ID from flickr obtained earlier to build a URL leading to that image on flickr which it tweets as well. The final part of the tweet is a url that leads to this post!

That was a lot of explanation, but this program does quite a bit. The source comes in two parts, here’s the python script that handles the brunt of the processing. You will need a bunch of libraries to run this, you could pick through past posts of mine to find what those are, but when I do a final post for this project I will include all of those.

Here’s the .php script that renders the graph from the mysql data. It is called by the python script.

Thanks for reading!

PiPlanter | Second round of data collection

So as I said in one of my previous posts, I am going to be collecting a lot of data over the next few weeks while the tomato plants grow. I will be doing this to determine when soil is “dry” and how temperature and light effect that process. For the last week I have been collecting data in the configuration seen in my last post and here is the graph it produced you can click to see the full image:

This graph proves a few things. The first thing is that the relative moisture sensor works. As one can intuitively understand, if you don’t add more water into the system, nature will remove water via evaporation. The overall trend of the blue line (the rel mst sensor) is downward, backing up this point.

The problem with this setup was that I was spitting the voltage across the two probes constantly, which along with the water caused the nails to rapidly oxidize, which is something I would like to avoid in the long term. This also may have seriously corrupted the data so besides general trends, this whole set is unusable.

This isn’t necessarily a bad thing though, as I wanted to conduct a second trial with more probes and more dirt.

I decided to go with 4 probes, and here are a few pictures of the assembly process. Assembly process is the same, I just did it at my school:

I cut it into 3cm sections and then drilled holes on the midpoints of the 2nd and 3rd cm as seen in a photo below.

Here are the holes drilled for the nails

Here are the nails inserted into all 4

Here is the wire wrapped around the nail

Once solder is applied, the connection is very strong and conductive

Here’s the gluing process

Here are all of the sensors assembled. I attached headers to the other ends as seen in the last post.

Since i’m using 4 sensors now, and to get around the oxidation problem, I added a NPN transistor to cut the ground current when the sensor isn’t being used so it only turns on when it’s getting polled. Here is the new python code:

It’s pretty much the same thing.

The graph is also very similar, but I won’t post that code as it’s not different enough.

Here are pictures of setting up the whole system:

I used the same soil as seen in the previous post, and added 125mL of water to each sample.

Here’s a video of me explaining the whole process:

Once enough data is collected I’ll post a graph of it here.

PiPlanter | Basic package setup and bringing everything together

I’m in a hotel trying to occupy myself with something interesting so I’ve decided to work on this. I had to re-image the SD card I’ve been developing this project on, but I saved to code so there’s no problem there. Now I need to re-install all the basic packages.

First I need to get the components of a LAMP server with the following commands:

Once you get the mysql server setup, you’ll need to create a database and tables in mysql.

To create the database you’ll be using run the following command:

And then grant the proper privileges to use later with the command:

Then we can enter the database and create a table:

Now we need to set up the specific libraries for python the first of which being spidev, the spi tool for the raspberry pi which we can grab from git using the following commands:

You also need to (copied from http://scruss.com/blog/2013/01/19/the-quite-rubbish-clock/):

As root, edit the kernel module blacklist file:

Comment out the spi-bcm2708 line so it looks like this:

Save the file so that the module will load on future reboots. To enable the module now, enter:

We will also need WiringPi:

Then you need to get APscheduler, the timing program used to execute the incremental timing with the following commands:

You will need mysqldb to interface python and mysql:

Once you reboot, the following program should work:

And there you go! The program should log data every minute and then every hour to two different tables. To view those data sets as php tables you can use this php script:

Sometime later I’ll get to graphing the data.

PiPlanter | Going from analog data to the web using python, mysql and php

Here’s a video:

Essentially, the adc reads an analog value and sends it to python. Python connects to a mysql database and adds the data to that. Once the user accesses the php script, a table, containing all of the values, is rendered.
First of all, you’ll need to make a mysql table with the correct specifications:

This creates a table that is found in the python script so it is very important that the TABLE values match as well as the column names.
Here’s that python script:

This reads the values from the adc (it is wired up as seen in this post)
Here’s the php, it reads the values from the adc_input_data_10 table on the adc_database.

This is all very preliminary stuff, and I’ll tighten up the code as time goes on.