Smarter Crop Predictions: Bridging Data and Plant Science
![The proposed AgriPINN model integrates deep learning with established crop physiology by embedding the LINTUL5 biomass-growth ordinary differential equation-described as [latex]\frac{d AGB}{dt}[/latex]-as a soft constraint within the neural network’s optimization process, simultaneously predicting above-ground biomass (AGB) alongside latent physiological variables such as leaf area index (LAI), radiation use efficiency (RUE), photosynthetically active radiation (PAR), and foliage water fraction, and then using the resulting process residual [latex]r(\mathbf{p},t)[/latex] to enforce biophysical consistency across space and time.](https://arxiv.org/html/2601.16045v1/x1.png)
A new hybrid AI framework, AgriPINN, combines the power of deep learning with established agricultural models for more accurate and interpretable crop biomass predictions under challenging conditions.






