The Crucial Role of Digital Twins in the Next Generation of Precision Agriculture

On By Mei Cooper

With traditional agricultural practices becoming increasingly unsustainable, scientists are constantly searching for ways to find economic and efficient methods of food production. Food insecurity is escalating across the globe, challenging agriculture companies to find radical new ways to efficiently produce crops with less waste, fewer pesticides, and a reduced carbon footprint.1 

To address these challenges, scientists are looking towards indoor farming techniques, such as controlled environment agriculture (CEA) or vertical farming. However, computer-aided methods and tools, such as digital twins, are necessary to make this possible.1 

CEA is the technique of growing crops in an isolated environment artificially controlled by complex machinery HVAC (heating, ventilation, and air conditioning), lighting system, irrigation, and an array of sensors to measure environmental conditions.1

These controlled environments, due to automation, are able to achieve better yield and quality than a traditional agricultural setting, while also reducing waste.1 

As the complexity of these improvements continues to increase, finding the optimal environmental conditions to appropriately stimulate growth while reducing energy consumption is posing a challenge to researchers.1 

This challenge requires continuous monitoring of the controlled environment, constant, real-time decision-making, and high-precision control of the environment- tasks that are best performed by computer-aided support, like digital twins.1

A digital twin is a virtual representation of an object or system that spans its entire lifecycle, is updated with real-time data, and uses simulation, machine learning, and reasoning to help decision-making.2

In CEA, digital twins are typically used for monitoring and controlling environmental conditions to best stimulate plant growth at an appropriate and sustainable rate. Though autonomy varies, digital twins can control the artificial environment directly.1

The main goal of using digital twins is to reduce energy consumption or to improve the crop-to-energy ratio, as heating and cooling at precision agriculture facilities consume copious amounts of energy. Furthermore, the data collected by digital twins, especially with the help of AI, can be used for designing new greenhouses. This data can be used for experiential purposes when new greenhouses are designed.1 

Though arguable whether developing and implementing digital twins is economically feasible, advancing digitalization in agriculture will help meet the goal of food security and sustainable production. With decreasing prices of hardware and computing power, digital twins in agriculture are becoming a reality.1

References

  1. David, I. (2023, November 24). How digital twins will enable the next generation of precision agriculture. Phys.Org. https://phys.org/news/2023-11-digital-twins-enable-generation-precision.html
  2. What is a digital twin? (n.d.). IBM. https://www.ibm.com/topics/what-is-a-digital-twin