AI-Enhanced Digital Cameras Revolutionize Soil Moisture Monitoring

The United Nations predicts that by 2050 many areas of the planet may not have enough fresh water to meet the demands of agriculture if we continue our current patterns of use. One solution to this global dilemma is the development of more efficient irrigation, central to which is precision monitoring of soil moisture, allowing sensors to guide ‘smart’ irrigation systems to ensure water is applied at the optimum time and rate.

Current methods for sensing soil moisture are problematic — buried sensors are susceptible to salts in the substrate and require specialized hardware for connections, while thermal imaging cameras are expensive and can be compromised by climatic conditions such as sunlight intensity, fog, and clouds.

Researchers from The University of South Australia and Baghdad’s Middle Technical University have developed a cost-effective alternative that may make precision soil monitoring simple and affordable in almost any circumstance. They successfully tested a system that uses a standard RGB digital camera to accurately monitor soil moisture under a wide range of conditions.

The system is based on a standard video camera that analyzes the differences in soil color to determine moisture content. It was tested at different distances, times, and illumination levels, and was found to be very accurate. The camera was connected to an artificial neural network (ANN), a form of machine learning software that the researchers trained to recognize different soil moisture levels under different sky conditions.

Using this ANN, the monitoring system could potentially be trained to recognize the specific soil conditions of any location, allowing it to be customized for each user and updated for changing climatic circumstances, 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. The system holds promise as a tool for improved irrigation technologies in agriculture in terms of cost, availability, and accuracy under changing climatic conditions.”