UC Merced Magazine | Volume XX, Issue VI

AI in Agricultural Research: Cultivating a Smarter Future

OPINION

By Professor Teamrat Ghezzehei Imagine a world where every blade of grass tells a story, where the soil beneath our feet holds secrets waiting to be unlocked and where arti cial intelligence helps us feed billions. is isn't science ction — it's cutting-edge reality being forged in the labs and elds of UC Merced. But how exactly is AI revolutionizing how we understand and manage a precious resource — our agricultural lands? In recent years, the Soil & Environmental Physics research group at UC Merced has embarked on applying the latest tools in machine learning for agricultural and soil science applications. We are harnessing the power of AI and machine learning to tackle some of the most pressing challenges in agricultural research. From predicting soil moisture to unraveling the complex dance of water through earth, our work is rede ning the boundaries of what's possible in modern farming. Picture this: A drone soars over a vast grassland, its multispectral cameras capturing images invisible to the human eye. is is no ordinary survey — it's a key part of our goal to predict soil moisture in grasslands using AI. Unlike uniform crop elds, grasslands are a complex tapestry of diverse plants and uneven terrain, making traditional moisture measurement methods a real head-scratcher. Our solution? Turn the vegetation into a moisture sensor, using AI to decode

hidden messages in those aerial images. But why stop at the surface? Dive with us into the world beneath our feet, where we are using AI to predict crucial soil properties such

as saturated hydraulic conductivity. It's a mouthful, sure, but think of it as the soil's ability to play water slide — vital for understanding how water moves through the ground. Our AI models are cracking this code faster and more accurately than ever, giving us unprecedented insights into the secret life of soil. Now hold onto your lab coats, because we're about to venture into the realm of physics-informed neural networks (PINNs). Imagine AI that not only learns from data but also understands the laws of physics. It's like teaching a computer to think like a scientist and a mathematician. With PINNs, we model water ow through soils in ways once thought impossible, especially in tricky scenarios such as layered soils with poorly de ned conditions at their boundaries that are a pain to solve with conventional methods. We also are ipping the script with inverse modeling. Instead of just predicting what might happen, we use AI to work backward from observations to uncover hidden soil properties. We use our

physics-informed models to turn routine measurements of soil moisture into deeper insights into soil characteristics. As we push the boundaries of what's possible with AI in agriculture, we can't help but get excited about the future. So, what's on the horizon? Imagine farmers making pinpoint decisions about irrigation and using their limited and precious water resources more judiciously. We are setting our sights on forecasting crop water needs, integrating satellite data with our models and exploring new frontiers in AI-driven agricultural research. As we face the monumental challenge of feeding a growing global population in a changing climate, the insights we are uncovering with AI will be more crucial than ever. At UC Merced, we are not just cultivating crops; we're cultivating a smarter, more sustainable future for agriculture. Teamrat Ghezzehei is a professor of soil physics in the School of Natural Sciences.

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