<ul><li>Process-based models (PBMs) and deep learning (DL) are two key approaches in agricultural modelling.</li><li>This study presents a review of PBMs, DL models, and hybrid PBM-DL frameworks in agricultural modelling.</li><li>Results demonstrate that hybrid models consistently outperform traditional PBMs and DL models.</li><li>The study contributes to the development of scalable, interpretable, and reproducible agricultural models for sustainable agriculture.</li></ul>

Deep Learning Meets Process-Based Models: A Hybrid Approach to Agricultural Challenges

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