Efficient irrigation requires precision monitoring of soil moisture, but current methods have their drawbacks.
Salts in the soil can negatively affect buried sensors, which also require specialised connectivity hardware.
Thermal imaging cameras are expensive and the accuracy of their results can be reduced by weather conditions such as fog and clouds.
Researchers from The University of South Australia and Baghdad's Middle Technical University have developed a cost-effective alternative using a standard RGB digital camera and machine learning that may make precision soil monitoring simple and affordable in almost any circumstance.
"The system we trialled is simple, robust and affordable, making it promising technology to support precision agriculture," said Dr Ali Al-Naji.
"It is based on a standard video camera which analyses the differences in soil colour to determine moisture content. We tested it at different distances, times and illumination levels, and the system was very accurate."
Images collected by the camera are processed by an artificial neural network trained to recognise different soil moisture levels under different sky conditions.
Such a monitoring system could be trained to recognise the specific soil conditions of any location, ensuing maximum accuracy.
"Once the network has been trained it should be possible to achieve controlled irrigation by maintaining the appearance of the soil at the desired state," said Professor Javaan Chahl.
"Now that we know the monitoring method is accurate, we are planning to design a cost-effective smart-irrigation system based on our algorithm using a microcontroller, USB camera and water pump that can work with different types of soils.
"This system holds promise as a tool for improved irrigation technologies in agriculture in terms of cost, availability and accuracy under changing climatic conditions."
The work represents a shift in focus for Professor Chahi and Dr Al-Naji, who are world leaders in the use of computer vision to monitor heart and respiratory rates. Professor Chahl has also worked on a computer vision system able to detect COVID-19 symptoms.