Machine learning offers growers a new tool for predicting crop water use

A new study shows that machine-learning models can accurately predict daily crop transpiration using direct plant measurements and environmental data. By training models on seven years of high-resolution lysimeter data, the researchers demonstrate strong performance across tomatoes, wheat, and barley. The findings point toward future tools that may support both irrigation management and early detection of plant stress.