Accurately predicting complex agronomic traits remains a major bottleneck in crop breeding. This study demonstrates how ...

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 ...

A new method predicts leaf optical properties from traits, improving canopy light modeling and photosynthesis estimates in ...

India’s crop protection landscape has fundamentally shifted- from protecting yields to securing food security amid climate ...

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

Artificial intelligence and machine learning play an increasingly important role. Algorithms trained on large datasets are ...

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

Artificial intelligence is quietly reshaping how crops are bred, and the biggest gains may come not in corn or wheat but in ...

For decades, soil management has relied on sparse field sampling and averaged recommendations. While effective in relatively uniform landscapes, this approach breaks down in real-world fields where ...

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

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 ...

A new study presents a zero-shot learning (ZSL) framework for maize cob phenotyping, enabling the extraction of geometric ...