The Chameleon is a resistance-type sensor that is calibrated to give the soil water tension or suction. It is different from a 'Gypsum Block' sensor. The Gypsum Block is not sensitive in the 'wetter part' of the soil moisture range (colours blue and green on the Chameleon) and therefore not suitable for monitoring most irrigated crops. The Chameleon sensor is comprised of an inner core of highly absorbent material that releases a lot of water in the 10 to 50 kPa suction range. This material effectively amplifies the soil water signal, so we get high resolution in the part of the soil moisture range most critical for irrigators. This inner core is coated with gypsum to provide buffering of electrical conductivity. The resistance reading is also corrected for changes in soil temperature.
Each soil type has a unique relationship between the soil water content (the amount of water in a given volume of soil) and the soil water suction (the water stress experienced by the plant). If you measure soil water content, there is a different 'number' for which you must irrigate each soil. If you measure soil water suction with a Chameleon sensor, the blue, green and red colours mean the same from the perspective of the plant stress, regardless of soil type. In other words, soil water suction sensors do not need to be calibrated for soil type.
Our aims is as follows: Blue 0-20 kPa (wet), Green 20 to 50 kPa (moist) and Red > 50 kPa (dry).
It is very difficult to make every sensor change exactly at those suction values, and for practical irrigation purposes, it is not necessary either.
Every sensor we build is individually tested to check the colours change in the correct range (that is why your sensor may look 'used').
Each sensor array is colour coded on the package after testing based on the blue to green switch point.
A yellow dot means the colour changed between 20 and 22 kPa.
An orange dot means the colour changed between 18 and 20 kPa.
A purple/pink dot means the colour changed between 22 and 24 kPa.
The expected lifespan of this sensor in the ground is 2 to 4 years, with the shorter time occurring in very wet, acidic or salty soils. The more recent designs are more durable for those who need to dig up and move sensors to other sites. As part of our research, we are modifying the design to increase accuracy and longevity.
The sensors are very dry when packaged for shipment and too dry for the reader to detect them.
When you try and read them straight out of the packet you may see the message on the reader screen "Check connections". This is because the reader can only detect the temperature sensor and not the soil water sensors.
Before you can read the sensors you must soak them in a bucket of water until they turn blue. See the sensor installation instructions here https://via.farm/chameleon-sensor-installation-instructions/
The grey colour indicates the sensor is disconnected.
When a sensor cable is disconnected (open circuit) the reader sees it as a very high resistance. On the Visualisation, anything about 4000 kOhms is assumed to be an open circuit and is colour grey to alert you to the fault. Please check your wiring, particularly at the green terminal block where it may have come loose. You will need the small flat screwdriver supplied with your reader to fix the problem.
Sometimes the soil gets so dry (end of the red zone) that it goes beyond the accuracy of our Chameleon reader. this also shows up as grey on the visualisation and the colour will return when the soil is wetted again.
If you buy Chameleon equipment from our online shop https://viashop.csiro.au/ you will be issued account details and a farm will be created for you within a few days of completing payment.
If you are using Chameleon equipment supplied via an ACIAR funded project then you should have been issued account details.
If you have not received your account details then please contact us via the https://via.farm/contact-us/ page.
Please provide this following and request a login:
You may want to export the sensor data into Excel or another program to analyse the data. You can do this following this method: