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.

翻译：我们提出了一种创新方法，利用物理引导神经网络（PGNN）来提升农业质量评估。该方法的核心是应用物理引导逆回归技术，该技术显著提升了模型精准预测作物质量指标的能力。该方法直接解决了传统评估方法在可扩展性、速度及实用性方面面临的挑战。通过将物理原理（尤其是菲克第二扩散定律）融入神经网络架构，我们开发的PGNN模型在提升评估可解释性与准确性方面取得了显著进展。针对黄瓜和蘑菇进行的实证验证表明，我们的模型在采后质量评估中优于传统计算机视觉技术。这凸显了我们作为可扩展且高效的解决方案，对应对全球粮食供应紧迫挑战的贡献。