A new study shows that machine-learning models can accurately predict daily crop transpiration using direct plant ...

A new Israeli study suggests that machine-learning models may soon give growers a far more precise way to predict how much water their crops use each day, while also laying the groundwork for earlier ...

By contrasting nematode infection with drought stress, the study highlights both the potential and limitations of remote sensing for separating ...

A research team has developed a new way to measure and predict how plant leaves scatter and reflect light, revealing that ...

A research team shows that phenomic prediction, which integrates full multispectral and thermal information rather than ...

Researchers in Slovakia have demonstrated a machine-learning framework that predicts PV inverter output and detects anomalies using only electrical and temporal data, achieving 100% accuracy in ...

By combining a custom-built optical instrument with physics-based modeling and machine learning, the study shows that leaf-level optical properties ...

Digital condition monitoring is transforming hydropower O&M with AI-driven diagnostics, hybrid architectures, and predictive ...

Vineyards in the U.S. and Europe are testing robots for disease monitoring and treatment amid climate stress and labor ...

This study shows that a blood-based metabolomic signature linked to maternal BMI predicts gestational diabetes and ...

A Hebrew University study suggests AI tools could help growers better manage water use by predicting healthy plant behavior and flagging early signs of stress.

A research team has developed a low-cost, high-throughput phenotyping platform that continuously measures plant transpiration, enabling real-time monitoring of drought response.