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UniSA develops smart irrigation technology

Researchers at UniSA have developed a cost-effective new technique to monitor soil moisture using a standard digital camera and machine learning technology.

The United Nations predicts that by 2050 many areas of the planet may not have enough freshwater 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 specialised 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.

A team including UniSA engineers Dr Ali Al-Naji and Professor Javaan Chahl has successfully tested a system that uses a standard RGB digital camera to accurately monitor soil moisture under a wide range of conditions.

“The system we trialled is simple, robust and affordable, making it promising technology to support precision agriculture,” Dr Al-Naji says. “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.”

The camera was connected to an artificial neural network (ANN) a form of machine learning software that the researchers trained to recognise different soil moisture levels under different sky conditions.

Using this ANN, the monitoring system could potentially be trained to recognise the specific soil conditions of any location, allowing it to be customised for each user and updated for changing climatic circumstances, ensuring 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.

Now that the team knows the monitoring method is accurate, they are planning to design a cost-effective smart-irrigation system based on their 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.

What is a Neural Network?

A neural network (also called an artificial neural network) is an adaptive system that learns by using interconnected nodes or neurons in a layered structure that resembles a human brain. A neural network can learn from data – so it can be trained to recognize patterns, classify data, and forecast future events.

A neural network breaks down the input into layers of abstraction. It can be trained using many examples to recognize patterns in speech or images, for example, just as the human brain does.

Its behaviour is defined by the way its individual elements are connected and by the strength, or weights, of those connections. These weights are automatically adjusted during training according to a specified learning rule until the artificial neural network performs the desired task correctly.

Neural networks are especially well suited to perform pattern recognition to identify and classify objects or signals in speech, vision, and control systems. They can also be used for performing time-series prediction and modelling.

Here are a few examples of how artificial neural networks are used:

  • Detecting the presence of speech commands in audio by training a deep learning model.
  • Applying the stylistic appearance of one image to the scene content of a second image using neural style transfer.
  • Converting handwritten Japanese characters into digital text.
  • Detecting cancer by guiding pathologists in classifying tumours as benign or malignant, based on uniformity of cell size, clump thickness, mitosis, and other factors.
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