This educational project is maintained by students and a work in progress. Final version is expected september 2025.
We are team "Terrain Explorers," conducting research for the ESA Climate Detectives competition.
Stay updated on our project and follow our progress here!
On our Chromebook, we experimented with Teachable Machine—a web-based tool by Google that lets anyone create machine learning models without writing a single line of code. We trained our model to recognize different images.
We modified a standard webcam by removing the IR filtering glass and adding polarizing filters, which enhance contrast in our photos. This makes it easier to distinguish real plants from artificial ones.
We formulated our research question and action plan and submitted them to ESERO. Next, we reached out to the weather station in Flobecq, but we are still awaiting a response regarding potential data collaboration.
We soldered the power supply to the solar panel, allowing it to run on solar energy. This eliminates the hassle of constantly replacing batteries.
The Low Warp ASA filament from REAL is the perfect material for outdoor prints. Its water-, UV-, and heat-resistant properties make it highly durable against moisture and weather conditions. Additionally, it offers excellent impact resistance, minimal shrinkage, and greater flexibility and hardness than ABS, making it a popular choice among users. It prints easily without a heated chamber and supports relatively high speeds. With its versatility, Low Warp ASA allows us to create strong, functional outdoor prints with ease.
3D design automatic pluviometer / reed contact
We designed a pluviometer in Tinkercad, which we later 3D-printed to measure rainfall. The reed switch was provided by our teacher, while the mounting component was also designed in Tinkercad by the same student.
Testing the soil sensor by dipping it in a small bucket of water, while simultaneously coding it, so the sensor doesn't give us the wrong results.
Difference between real and fake plants
A new student used our modified webcam and Teachable Machine to tell real plants from fake ones. The polarizing filters in the webcam improve contrast by allowing specific light directions, helping the machine learning model to learn and make accurate distinctions between the real and fake plants.
Coding the pluviometer to count how many times the bucket tips, this can let us know how much rainfall there was.
Advice from experts from KMI & VITO!
We arranged a meeting with an expert from KMI where we introduced our project and asked questions that could possibly help us further.
Later we had another meeting with an expert from VITO who also gave us helpful advice for our project.
9 January '25
During the Christmas holidays, there were hours of 3D-printing to have all the pieces ready in time. Now we can start assembling the final sensor.
The code has been carefully tested, and where necessary changes have been made to ensure optimal performance and accuracy. In addition, the pluviometer is fully wired, with all connections properly made and checked to ensure reliable operation of the device.
We connected the keyboard, mouse, and camera. We made the code to take pictures with the Pi noir camera.
We tested the Pi noir camera to see what the photos looked like. With a Pi noir camera, you can also see in the dark with infrared lighting. The infrared can make the photos look strange during the day.
We made a sketch for the complete wiring of different sensors, we will connect these on a printed circuit board with each other and with the raspberry pi pico. Furthermore we also start with soldering the first wires.
We continued working on the website. They collaborated to implement new features, resolve existing issues, and ensure that the website met the desired standards of quality and usability.
Soldering wiring diagram
We continued to solder the wires between the OLED-display, rain gauge, soil moisture sensor and the Raspberry Pi Pico. This was all connected to each other on a printed circuit board.
6 february '25
We explained the different steps of the project in a video, so people can understand what we did.
6 February '25
We worked together on how to let the raspberry pi take photos on it's own, without any human control.
6 February '25
We tried to measure how many milliliters of water it took for the pluviometer to tilt. We ended figuring out that the quantity differed because of friction.
Per measurement, we poured 25ml of water using a stand and buret. We measured a total of 5 times.
1st time: it tilted 14 times
2nd time: 14 times
3rd time: 11 times → a lot more water was necessary on the right side to tilt.
4th time: 8 times → after the first tilt it would create friction which caused the next one to take way more water and overflow, it became very inconsistent.
5th time: 9 times → same as the 4th, friction that caused the measurements to be off.
For next time, we have an idea which could help make the measurement more consistent and better.
13 February '25
Today there was a strike. The teacher wasn't there, so we did some research with the entire group.
PUTTING THE PROJECT TOGETHER
13 march '25
The other part of the class put together the project and hung the solar panels on the stand
20 march '25
We found satellite data on Terrascope on the location where a student’s family runs a farm in Everbeek-Beneden (Brakel). This was the perfect opportunity to use their plots for our project.
We received offcial weather data at the KMI for the nearest station in Chièvres for our test location. This is aprox 20 km from the our test field. We turned this data to graphs.
At our testsite, maize and potatoes are grown. These crops are sensitive to both drought and excessive rainfall. Potatoes, in particular, require a regular water supply, but too much water can lead to root rot. Grain crops, on the other hand, vary in their tolerance depending on the type of grain. Some types are more resistant to water, while others can better withstand drought.
27 march '25
We compared the satellite data from early October with that from December. it is was noticeable that there are more yellow plots in December, including some of our plots.
Farmers can use non-inversion tillage to improve soil structure and water retention, though its benefits appear after several years and require new equipment.
Adjusting soil permeability with materials like sand helps prevent waterlogging.
Management agreements encourage nature-friendly measures like grass strips to manage water and support biodiversity.
These actions help make farming more sustainable and resilient to extreme weather.