Digital Twinning: Emerging Technology Could Revolutionize Agriculture, Researchers Find

Aug 28, 2023

By Emma Bartlett

Picture this: a herd of livestock gather at their feeding and milking stations where sensors have been installed to monitor their health and behavior. Through the continuous collection of data, farmers remotely monitor the herd’s health, identify potential problems, create simulations to improve livestock treatments, and model the best barn systems to maintain air quality and temperature. All this remote management is made possible through a technology known as digital twinning.

Introduced in 2003, digital twins are virtual replicas of physical objects — in this instance, farmers — that are remotely linked to real objects; these copies collect data and simulate real situations and their outcomes so individuals can make better decisions. The technology’s popularity increased at the start of the pandemic and, in 2022, was valued at $6.9 billion; this number is expected to jump to $73.5 billion by 2027.

While digital twinning is predominately used in manufacturing and engineering, Bryant Marketing Professor Sharmin Attaran, Ph.D., and two outside researchers recently explored how this technology can be used to revolutionize the agriculture industry. The three found that digital twins are helpful in mitigating shortfalls in production, anticipating output, reducing risk of crop failures, and monitoring crop growth and health — all of which was published in the Encyclopedia of Information Science and Technology.

“Digital twins offer a transformative approach to agriculture by enabling farmers to closely monitor crops, soil, and weather, ensuring optimized irrigation and resource use. This leads to cost savings and heightened productivity, enhancing the sector's sustainability,” says Attaran. This information then helps the farmer improve management decisions like crop rotations plans, anticipated over- or under-production, and ways to address shortfalls in production.

Today, the United States is one of the world’s leading producers of food and agriculture products. With more people to feed, a higher demand for agricultural products, and increased concern over food quality and safety, the agriculture industry is under significant economic pressure to increase productivity and efficiency while maintaining profitability. To meet these challenges, researchers note that the industry needs new technologies, techniques, and information — such as digital twinning — to improve their yields and manage their resources more efficiently.

“In essence, digital twins in agriculture offer a holistic approach to farm management, resource use, predictive insights, and environmental adaptability,” Attaran says, adding how saving time and costs will improve sustainability and attract higher premiums for produce.

Researchers explain that farming processes are complex and dynamic because they depend on natural conditions. This technology, Attaran adds, aids in predicting weather patterns and understanding the long-term implications of climate change while highlighting stress points in agricultural systems caused by factors like soil quality or pollution.

“By analyzing real-time data from digital twins, farmers can foresee and react to various challenges, such as potential crop diseases, yield expectations, or equipment malfunctions,” Attaran says.

Digital twins also improve traceability and transparency in agriculture’s supply chain management by helping farmers anticipate disruptions, identify optimum transportation/logistics routes, and create contingency plans. While there is room for future research on digital twins, the technology’s future is looking bright. Farmers are open to innovation in agriculture technology to optimize their returns and minimize financial risk, according to a McKinsey & Company article, but the adoption of these emerging tools remains slow.

“Adopting digital twin solutions propels agriculture into a new era of digital efficiency and sustainable profitability,” Attaran says.

Source : bryant.edu
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