Search Menu
Photonics Media Photonics Marketplace Photonics Spectra BioPhotonics Vision Spectra Photonics Showcase Photonics ProdSpec Photonics Handbook

Standard Digital Camera and AI Pair for Soil Monitoring

Facebook Twitter LinkedIn Email
Digital cameras and machine learning technology can provide a cost-effective method of monitoring soil moisture, according to new work from the University of South Australia and Middle Technical University (Iraq). The technology aims to address current difficulties with sensing soil moisture, such as buried sensors that are vulnerable to salts in the substrate and require specialized hardware or connections, and expensive thermal imaging cameras that can be susceptible to climatic conditions such as sunlight intensity, fog, and clouds.

The team’s approach uses a standard RGB digital camera to accurately monitor soil moisture under a wide range of conditions, with help from machine learning software.
Image showing the system function of UniSA and Middle Technical Uni's computer vision for smart irrigation. Courtesy of Ali Al-Naji.
Image showing the system function of University of South Australia and Middle Technical University’s computer vision for smart irrigation. Courtesy of Ali Al-Naji.

“The system we trialed is simple, robust, and affordable, making it promising technology to support precision agriculture,” said Ali Al-Naji, a professor at the University of South Australia. “It is based on a standard video camera that analyzes the differences in soil color to determine moisture content. We tested it at different distances, times, and illumination levels and the system was very accurate.”

The team connected the camera to an artificial neural network (ANN). The system was trained to recognize different soil moisture levels independent of any sky condition. The ANN allows the system to recognize the specific soil conditions of any location; it can be customized for each user and updated for changing climatic circumstances to ensure 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 Javaan Chahl, a professor at the University of South Australia. “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 soil.”

The system, Chahl added, has potential as a cost-effective, accurate, and, in terms of its components, readily available tool for improved irrigation technology under changing climate conditions.

The research was published in Heliyon (
Mar 2021
Denoting the use of binary notation; i.e., the representation of data by bits (1 or 0).
Referring to the bandwidth and spectrum location of the signal produced by television or radar scanning.
machine vision
Interpretation of an image of an object or scene through the use of optical noncontact sensing mechanisms for the purpose of obtaining information and/or controlling machines or processes.
Research & TechnologyANNneural networkneural networksartificial neural networkscamerasdigitalVideosoilsoil analysismoisturemonitormonitoringUniversity of South Australiamachine visionimagingAustraliaagriculture

Submit a Feature Article Submit a Press Release
Terms & Conditions Privacy Policy About Us Contact Us
Facebook Twitter Instagram LinkedIn YouTube RSS
©2023 Photonics Media, 100 West St., Pittsfield, MA, 01201 USA, [email protected]

Photonics Media, Laurin Publishing
x We deliver – right to your inbox. Subscribe FREE to our newsletters.
We use cookies to improve user experience and analyze our website traffic as stated in our Privacy Policy. By using this website, you agree to the use of cookies unless you have disabled them.