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

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

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

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

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

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

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 ...

A research team has developed a new hybrid artificial intelligence framework that can accurately estimate leaf nitrogen ...

Leibniz-Centre for Agricultural Landscape Research (ZALF) e.V. Email: stefan [dot] stiller [at] zalf [dot] de, stillsen [at] gmail [dot] com This repository contains the code for the study ...

Abstract: Accurate in-season crop yield prediction is critical for timely agricultural decision-making, food security, and climate-resilient farm management. This study presents a framework for ...

The models were built and deployed by NOAA's Environmental Modeling Center in coordination with the National Weather Service. A spokesperson for the service, Erica Grow Cei, ...

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 ...