Crop farmers spearheaded by Texas A&M AgriLife Research, are using remote sensing, big data and artificial intelligence to simulate and predict real-world crop production scenarios.

Precision agriculture new frontier: Crop digital twins

Crop farmers in South Texas are witnessing the future of agriculture unfold with the advent of digital-twin technology. Spearheaded by Texas A&M AgriLife Research, this cutting-edge approach combines remote sensing, big data and artificial intelligence to simulate and predict real-world crop production scenarios.

Juan Landivar, Ph.D., director of the Texas A&M AgriLife Research and Extension Center at Corpus Christi, leads a multidisciplinary team of experts, including agronomists, computer engineers, electrical engineers and civil engineers.

He recently shared their findings at the Texas Plant Protection Association Conference, emphasising this technology’s transformative potential. Their methods and results have been published in the peer-reviewed journal Computers and Electronics in Agriculture.

The birth of an idea

The concept of digital-twin technology in agriculture emerged from a conversation six years ago between Juan and his then-colleague Jinha Jung, Ph.D., now an associate professor at Purdue University.

“We were returning from a meeting when the idea clicked,” Juan recalled. “I couldn’t sleep that night. By 3 am, I was texting Jinha, realising the vast opportunities this technology could unlock for agriculture.”

This sparked a series of trials on a 200-acre farm in South Texas, cultivating cotton and sorghum, which have showcased the technology’s promise. Using drones, the team gathered over 250,000 data points in a single season, measuring canopy cover, plant height and vegetation indices via normalised difference vegetation index, NDVI.

The challenge then became how to interpret this massive data trove.

Power of AI

“That’s where our AI-powered web-based modeling comes in,” Juan said. “It translates complex datasets into actionable insights for farmers, helping with decisions on yield prediction, biomass estimation, crop termination and irrigation scheduling.”

One notable success involved advising a farmer to prepare for harvest earlier than expected. In the 2024 cotton crop, AI modeling accurately predicted optimal harvest preparation as early as June 18.

“The farmer said ‘no way. I usually defoliate in July,’” Juan recalled, “but field observations on June 24 confirmed the model’s accuracy.”

“Somewhere along there, they had several inches of rain and delayed defoliation,” he said. “But while waiting for the soil to dry, heavy rains from an approaching hurricane came through and dropped another four inches. Harvest wasn’t until late July, losing quality and about $70 per acre in potential profit.”

Benefits for farmers

Digital-twin technology is ushering in an era of prescriptive agriculture, where decisions are data-driven rather than guesswork. For instance, early yield forecasts — available six to eight weeks before harvest — can aid financial planning and market strategies.

“This precision saves costs and maximises harvest potential,” Juan said. “It also supports sustainability goals, like estimating biomass for carbon credit markets.”

Looking ahead

The affordability of advanced tools like multispectral cameras has accelerated data collection and analysis, making technologies that once seemed out of reach more accessible.

“We’ve come a long way,” Juan said. “What used to be a luxury is now a necessity for modern farming.”

As this technology evolves, it holds immense promise for agriculture worldwide, Juan said. By empowering farmers with real-time insights and predictive analytics, digital twins are not just recreating crops — they are reshaping the future of farming.