Most irrigation decisions are made the same way they've been made for decades: by feel, by schedule, or by rule of thumb. A farmer waters on Tuesday and Saturday because that's what they've always done. Or they water when the leaves start to droop — a sign that the plant is already under stress.
This approach isn't negligent. For most of agricultural history, it was the best available method. But it wastes water on a massive scale, and in a world where freshwater resources are under increasing pressure, the gap between how much water we use and how much we actually need has become a problem worth solving.
The information problem
Optimal irrigation depends on dozens of interacting variables: soil type, crop growth stage, local weather patterns, root depth, evapotranspiration rates. No human can mentally integrate all of these in real time. And even if they could, the information — real-time soil moisture data, weather forecasts, satellite imagery — has historically been difficult to access.
What physics-informed models add
Standard machine learning models for irrigation tend to be data-hungry and black-box. Physics-informed neural networks (PINNs) solve this by embedding physical laws — the equations governing fluid dynamics and heat transfer in soil — directly into the model architecture. The result is a system that generalizes better to novel conditions and requires less training data. For resource-constrained agricultural contexts, that distinction matters enormously.