Connexion
We’re not going to lie — the thing we like most about this automated plant watering project is the timelapse at the very end of the build video. But we also thought now might be a good time to show you another Raspberry Pi project for keeping an eye on your plants, since some of us are getting back to something more like our usual routines and our houseplants are no longer our best friends, so they might need a little extra automated attention.
Maker Christopher Barnatt chose Raspberry Pi Zero for this project because although Raspberry Pi Pico could handle it, he needed a camera connector to record timelapse video of his plants’ growth.
Christopher is a gem and has included links to all the hardware he used. There are also some cheaper, smaller alternatives listed in the info section of his build video.
Kit list:
The moisture sensor checks every half hour to determine whether or not the plant has enough water, and communicates with the Raspberry Pi. Water flow is controlled by the solenoid valve, and if the Raspberry Pi finds the soil is too dry, it opens the valve for a set amount of time to let water out.
Christopher has shared all the code you need to make your own plant watering system:
Check out Christopher’s YouTube channel Explaining Computers where he posts new videos every week on topics like PC hardware, single board computers such as Raspberry Pi, AI, Big Data, and quantum computing.
The post Raspberry Pi Zero waters your plants and records growth timelapse appeared first on Raspberry Pi.
Bonsai trees are the most glorious of miniature shrubbery. But caring for them takes seriously green fingers. Luckily, this Raspberry Pi–powered bonsai watering system doesn’t require much to get started. Also, the Reddit user who shared the project is named Lord-of-the-Pis, so, we love.
The Pimoroni Explorer HAT Pro isn’t essential to make this project work, it just makes things a whole lot easier by removing the need for a relay. It also comes with a Python library for interfacing with Raspberry Pi. The project uses an I2C connection, so it would also be possible to not use the HAT and instead plug a moisture sensor into an analogue-to-digital converter and then into Raspberry Pi’s GPIO pins.
Lord-of-the-Pis explains: “I used the Pimoroni Explorer HAT Pro in order to make the entire system on a small breadboard on top of Raspberry Pi. The Explorer HAT has inbuilt analogue inputs over I2C, which I used for the input of the moisture sensor (two wires pushed into the soil as probes). Furthermore, the output GPIO pins on this HAT sink all current to ground when activated so they can be used as a transistor to power the small 5V motor (which was also attached to the 5V power pins on Raspberry Pi).”
Using the HAT also allowed this maker to simply hook the pump up to the GPIO pins and turn these on and off, so there’s no need for an on/off switch.
This project’s code is in Python 3, and you can find it all on GitHub.
The main watering program (plantWater.py) takes input from the moisture sensor, and if the soil moisture level is below a set amount, the bonsai gets watered.
Lord-of-the-Pis built a simple web interface for the project on a localhost site that’s hosted using Apache. Apache SSI is used to execute the Python scripts. Due to the use of SSI, the index page is called index.shtml.
An image of the website. The Dip and then steadiness of the graph is due to the faulty moisture sensor. The maker has ordered another!
A lot more detail about the hardware and software involved is available in this second reddit post about the project.
Lord-of-the-Pis is now working on a dashboard that plots the soil moisture over time, as well as tracking other things like light intensity, temperature, and humidity.
May no other plant perish due to overwatering on our watch ever again!
The post Raspberry Pi–powered bonsai watering system appeared first on Raspberry Pi.
We love seeing Raspberry Pi being used to push industry forward. Here’s an example of how our tiny computers are making an impact in agriculture.
Directed Machines is a small company on a mission to remove pollution and minimise human labour in land care. Their focus is to do more with less, so the affordable power of our robust computers matches perfectly with their goals.
You’ll find a Raspberry Pi 4 at the heart of their solar-powered, autonomous, electric tractors called Land Care Robots.
Here are a few of the robot’s specs:
Directed Machine’s COO Wayne Pearson explains: “Rather than opting for the most advanced components (often the simplest solution), we endeavour to find affordable, easily sourced components. We then enable these components to accomplish more by ensuring efficient uses of compute/memory resources through our software stack, which we built from the ground up.”
“All in all,” Wayne continues, “this approach helps minimise unnecessarily inflated component costs (as well as the corresponding complexities) from being passed along to our customers — which keeps our prices lower and enables rapid field repair/maintenance.”
Here’s a practical example of that. This is a custom HAT Directed Machine’s ‘Electrical Engineering Guy’ Chris Doughty shared on LinkedIn. It was specially created to expand the functionality of the Raspberry Pi 4s they were using:
The HAT includes:
• 7-port USB 2.0 hub (six ports off-board) with individual port-power control
• 5A of 5.45V power to keep Pi running stable with high-current peripherals
• 9-axis IMU LSM9DS1
• Precision ‘M8P’ UBLOX GNSS receiver (capable of supporting RTK) SMA connection for external GPS antenna including DC for LNA
• 7–15V DC input to support automotive and accessory-port applications • Connects to standard Raspberry Pi 3 and 4 via pin-header and standoffs
Directed Machine’s founder George Chrysanthakopoulos shared the video at the top of this post on LinkedIn to demonstrate how the land care robots see the world while autonomously navigating. The combined power of Raspberry Pi 4 and their own built-from-the-ground software stack lets the robots see dual depth and colour streams at 15Hz. This is all made possible with a cheap GPS plus an Inertial Measurement Unit (IMU) for just $15 combined.
With a base price of the Land Care Robot is in the thousands, we’re not suggesting you should pick up one for your back garden — cutting the lawn is a childhood chore for the ages. But, for industry, the robot is a fine example of how businesses are using Raspberry Pi to cut both cost and environmental impact.
Also see Liz’s favourite project, the Cucumber Counter, and the popular CNC FarmBot, for more examples of ‘Down on the farm with Raspberry Pi’.
The post Raspberry Pi–powered robot farmers appeared first on Raspberry Pi.
Tired of losing vegetable crops to frequent summertime hail storms, Nick Rogness decided to build something to protect them. And the result is brilliant!
Digital Garden with hail protection
Tired of getting your garden destroyed by hail storms? I was, so I did something about it…maker style!
“I live in a part of the country where hail and severe weather are commonplace during the summer months,” Nick explains in his Hackster tutorial. “I was getting frustrated every year when my wife’s garden was get demolished by the nightly hail storms losing our entire haul of vegetable goodies!”
Nick drew up plans for a solution to his hail problem, incorporating liner actuators bolted to a 12ft × 12ft frame that surrounds the vegetable patch. When a storm is on the horizon, the actuators pull a heavy-duty tarp over the garden.
Nick connected two motor controllers to a Raspberry Pi Zero W. The Raspberry Pi then controls the actuators to pull the tarp, either when a manual rocker switch is flipped or when it’s told to do so via weather-controlled software.
“Software control of the garden was accomplished by using a Raspberry Pi and MQTT to communicate via Adafruit IO to reach the mobile app on my phone,” Nick explains. The whole build is powered by a 12V Marine deep-cycle battery that’s charged using a solar panel.
You can view the full tutorial on Hackster, including the code for the project.
The post Protect your veggies from hail with a Raspberry Pi Zero W appeared first on Raspberry Pi.
Chris Aviles, aka the teacher we all wish we’d had when we were at school, discusses how his school is in New Jersey is directly linking data with life itself…
Over to you, Chris.
Every year, our students take federal or state-mandated testing, but what significant changes have we made to their education with the results of these tests? We have never collected more data about our students and society in general. The problem is most people and institutions do a poor job interpreting data and using it to make meaningful change. This problem was something I wanted to tackle in FH Grows.
FH Grows is the name of my seventh-grade class, and is a student-run agriculture business at Knollwood Middle School in Fair Haven, New Jersey. In FH Grows, we sell our produce both online and through our student-run farmers markets. Any produce we don’t sell is donated to our local soup kitchen. To get the most out of our school gardens, students have built sensors and monitors using Raspberry Pis. These sensors collect data which then allows me to help students learn to better interpret data themselves and turn it into action.
In the greenhouse, our gardens, and alternative growing stations (hydroponics, aquaponics, aeroponics) we have sensors that log the temperature, humidity, and other important data points that we want to know about our garden. This data is then streamed in real time, online at FHGrows.com. When students come into the classroom, one of the first things we look at is the current, live data on the site and find out what is going on in our gardens. Over the course of the semester, students are taught about the ideal growing conditions of our garden. When looking at the data, if we see that the conditions in our gardens aren’t ideal, we get to work.
If we see that the greenhouse is too hot, over 85 degrees, students will go and open the greenhouse door. We check the temperature a little bit later, and if it’s still too hot, students will go turn on the fan. But how many fans do you turn on? After experimenting, we know that each fan lowers the greenhouse temperature between 7-10 degrees Fahrenheit. Opening the door and turning on both fans can bring a greenhouse than can push close to 100 degrees in late May or early June down to a manageable 80 degrees.
Turning data into action can allow for some creativity as well. Over-watering plants can be a real problem. We found that our plants were turning yellow because we were watering them every day when we didn’t need to. How could we solve this problem and become more efficient at watering? Students built a Raspberry Pi that used a moisture sensor to find out when a plant needed to be watered. We used a plant with the moisture sensor in the soil as our control plant. We figured that if we watered the control plant at the same time we watered all our other plants, when the control plant was dry (gave a negative moisture signal) the rest of the plants in the greenhouse would need to be watered as well.
This method of determining when to water our plants worked well. We rarely ever saw our plants turn yellow from overwatering. Here is where the creativity came in. Since we received a signal from the Raspberry Pi when the soil was not wet enough, we played around with what we could do with that signal. We displayed it on the dashboard along with our other data, but we also decided to make the signal send as an email from the plant. When I showed students how this worked, they decided to write the message from the plant in the first person. Every week or so, we received an email from Carl the Control Plant asking us to come out and water him!
If students don’t honour Carl’s request for water, use data to know when to cool our greenhouse, or had not done the fan experiments to see how much cooler they make the greenhouse, all our plants, like the basil we sell to the pizza places in town, would die. This is the beauty of combining data literacy with a school garden: failure to interpret data then act based on their interpretation has real consequences: our produce could die. When it takes 60-120 days to grow the average vegetable, the loss of plants is a significant event. We lose all the time and energy that went into growing those plants as well as lose all the revenue they would have brought in for us. Further, I love the urgency that combining data and the school garden creates because many students have learned the valuable life lesson that not making a decision is making a decision. If students freeze or do nothing when confronted with the data about the garden, that too has consequences.
The other major way we use data in FH Grows is to spot trends and make predictions. Different to using data to create the ideal growing conditions in our garden every day, the sensors that we use also provide a way for us to use information about the past to predict the future. FH Grows has about two years’ worth of weather data from our Raspberry Pi weather station (there are guides online if you wish to build a weather station of your own). Using weather data year over year, we can start to determine important events like when it is best to plant our veggies in our garden.
For example, one of the most useful data points on the Raspberry Pi weather station is the ground temperature sensor. Last semester, we wanted to squeeze in a cool weather grow in our garden. This post-winter grow can be done between March and June if you time it right. Getting an extra growing cycle from our garden is incredibly valuable, not only to FH Grows as business (since we would be growing more produce to turn around and sell) but as a way to get an additional learning cycle out of the garden.
So, using two seasons’ worth of ground temperature data, we set out to predict when the ground in our garden would be cool enough to do this cool veggie grow. Students looked at the data we had from our weather station and compared it to different websites that predicted the last frost of the season in our area. We found that the ground right outside our door warmed up two weeks earlier than the more general prediction given by websites. With this information we were able to get a full cool crop grow at a time where our garden used to lay dormant.
We also used our Raspberry Pi to help us predict whether or not it was going to rain over the weekend. Using a Raspberry Pi connected to Weather Underground and previous years’ data, if we believed it would not rain over the weekend we would water our gardens on Friday. If it looked like rain over the weekend, we let Mother Nature water our garden for us. Our prediction using the Pi and previous data was more accurate for our immediate area than compared to the more general weather reports you would get on the radio or an app, since those considered a much larger area when making their prediction.
It seems like we are going to be collecting even more data in the future, not less. It is important that we get our students comfortable working with data. The school garden supported by Raspberry Pi’s amazing ability to collect data is a boon for any teacher who wants to help students learn how to interpret data and turn it into action.
Issue 10 of Hello World magazine is out today, and it’s free. 100% free.
Click here to download the PDF right now. Right this second. If you want to be a love, click here to subscribe, again for free. Subscribers will receive an email when the latest issue is out, and we won’t use your details for anything nasty.
If you’re an educator in the UK, click here and you’ll receive the printed version of Hello World direct to your door. And, guess what? Yup, that’s free too!
What I’m trying to say here is that there is a group of hard-working, passionate educators who take the time to write incredible content for Hello World, for free, and you would be doing them (and us, and your students, kids and/or friends) a solid by reading it :)
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