Drastic climate change and overpopulation have made traditional agricultural production unsustainable. Even in more affluent countries, household food security is on the rise.
In Canada, for example, one in six households struggles to access the food they need to maintain a healthy and active lifestyle, and the situation is getting worse every year.
Food insecurity is a rapidly escalating global problem, and agricultural companies are challenged to find new and efficient ways to produce crops: less waste, fewer pesticides, faster time to market, and a smaller energy footprint.
Indoor farming techniques, such as controlled environment agriculture (CEA), are gaining traction as traditional outdoor farming is unable to meet these challenges.
However, these technologies require appropriate computer-aided support. We have developed such computer-aided methods and tools in the Sustainable Systems and Methods (SSM) Laboratory at McMaster University.
Introducing digital twins
A controlled environment refers to the technology of growing crops in an isolated environment that is artificially controlled by complex machines HVAC (heating, ventilation and air conditioning), irrigation and lighting systems, and a series of sensors that measure environmental conditions.
Thanks to automation, controlled environments achieve better yields and quality than traditional farming environments, while also reducing waste.
Because these improvements also add complexity, finding the optimal growth strategy – i.e., the sequence of environmental conditions that stimulate growth at the most appropriate rate and reduce energy consumption – is particularly challenging.
This is another complex challenge that requires continuous monitoring, real-time decision-making, and high-precision control of the environment, all tasks beyond the limits of human capabilities.
Computer-aided support such as digital twins can play an important role.
A digital twin is a digital representation of a physical object, person, or process that aids decision-making through high-fidelity real-time simulations of physical systems, often equipped with autonomous control capabilities.
In precision agriculture, digital twins are often used to monitor and control environmental conditions to promote the growth of crops at an optimal and sustainable rate.
The digital twin provides a real-time dashboard for observing the environmental conditions of the growing area, and the digital twin can also directly control the environment with varying degrees of autonomy.
Reducing energy consumption – or increasing the energy ratio of crops – is an obvious goal for precision agriculture facilities, as heating and cooling facilities consume a lot of energy.
Digital twins can also be used to design new types of greenhouses. For example, when designing a new greenhouse, a digital twin of the long-term data collected in the greenhouse can be used for experimental purposes.
Is it economically viable?
Developing digital twin systems and increasing the digital maturity of agricultural companies are the major factors influencing the cost of digitally enhanced CEA adoption.
The costs associated with developing a digital twin system mainly involve hardware components and software development. Making your own solution, experimenting with inexpensive equipment, and gradually expanding your capabilities is a good place to start and help you adopt the right digital mindset.
However, professional growing environments require industrial-grade subsystems that are completely incomparably costly, and the adoption of these systems requires a well-planned organization's digital strategy.
Agriculture is one of the least digitally advanced sectors, and digital maturity is an absolute prerequisite for the adoption of digital twins. As a result, the costs associated with digital maturity often outweigh the costs of technology in the field of smart agriculture.
A company in the early stages of digitalization must consider issues such as choosing a cloud provider, developing a data strategy, and acquiring a range of software licenses, to name just a few of the many serious challenges.
Organizational costs are difficult to assess, but can darken a company's economic outlook. This is a particularly painful stage of growth that requires proper counseling.
There have been some recent successful examples of industry-academia collaborations that have helped agricultural companies expand their ongoing digitalization efforts.
What's next?
The need for food security and sustainable production is as urgent as ever.
In the face of drastic climate change, wildfires across Canada, extreme air pollution in Europe, the ongoing energy crisis, and a growing population, food self-sufficiency has become one of humanity's top goals.
To achieve the second Sustainable Development Goal (SDG) of the United Nations General Assembly, to end global hunger by 2030, a radical transformation of agricultural models is necessary.
One way to achieve this ambitious goal is through advanced digitalization and digital twins.
There are still many hurdles to overcome before this can be achieved, but as hardware and computing power** continue to decrease, digitally-driven smart agriculture is becoming a reality.