  We present an innovative approach leveraging Physics-Guided Neural Networks
(PGNNs) for enhancing agricultural quality assessments. Central to our
methodology is the application of physics-guided inverse regression, a
technique that significantly improves the model's ability to precisely predict
quality metrics of crops. This approach directly addresses the challenges of
scalability, speed, and practicality that traditional assessment methods face.
By integrating physical principles, notably Fick`s second law of diffusion,
into neural network architectures, our developed PGNN model achieves a notable
advancement in enhancing both the interpretability and accuracy of assessments.
Empirical validation conducted on cucumbers and mushrooms demonstrates the
superior capability of our model in outperforming conventional computer vision
techniques in postharvest quality evaluation. This underscores our contribution
as a scalable and efficient solution to the pressing demands of global food
supply challenges.
