Most irrigation decisions are made the same way they have been made for decades: by feel, by schedule, or by rule of thumb. A farmer waters on Tuesday and Saturday because that is what they have always done. Or they water when the leaves start to droop, which is a sign that the plant is already under stress.
This approach is not 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, and evapotranspiration rates. No human can mentally integrate all of these in real time. And even if they could, the information they would need, including real-time soil moisture data, weather forecasts, and 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 hard to interpret. Physics-informed neural networks 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.