A combination of high resolution multi-spectral imagery and supervised learning effectively distinguishes between pasture species and seasonal changes in botanical composition.

Modeling the activity of enzymes in the Calvin Benson Cycle identifies key enzymes to improve photosynthesis.

How can plant modelers introduce a genetic dimension into physiological models at different organizational scales in a meaningful way?

A novel modelling approach explores how resource availability, competition pressure and insect herbivore pressure drive selection.

A new system automatically transforms existing process-based crop models into different languages and simulation platforms.

in silico Plants is pleased to welcome submissions for an upcoming virtual special issue on Functional-Structural Plant Modelling.

The editors of in silico Plants are delighted to share with you an Editor's Choice selection of key papers that highlight some of the best current plant modelling research published in the journal.

A new machine learning strategy allows genomic knowledge to be transferred from information-rich to information-poor species.

TF DEACoN is an in silico “screen” to identify candidate transcription factors using a public database.

Credibility and transdisciplinarity play crucial roles in modelling to advance crop adaptation and plant improvement.

Global sensitivity analysis was used to identify key traits for Eucalyptus adaptation to climate variability and change in Brazil.

Having your paper accepted is not the end of the production process.