# A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning

arXiv: https://arxiv.org/abs/2603.15411 · HTML: https://mlsanity.com/paper/2603.15411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)
- [Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems](https://mlsanity.com/paper/2003.04919/) (2020)
- [Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles](https://mlsanity.com/paper/2001.11086/) (2020)
- [Mechanistic?](https://mlsanity.com/paper/2410.09087/) (2024)
- [Multi-Task Learning for Sequence Tagging: An Empirical Study](https://mlsanity.com/paper/1808.04151/) (2018)
- [Prediction Under Uncertainty with Error-Encoding Networks](https://mlsanity.com/paper/1711.04994/) (2017)
- [Grape Cold Hardiness Prediction via Multi-Task Learning](https://mlsanity.com/paper/2209.10585/) (2022)
- [Differentiable modeling to unify machine learning and physical models and advance Geosciences](https://mlsanity.com/paper/2301.04027/) (2023)
- [Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy](https://mlsanity.com/paper/2501.16848/) (2025)
- [WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies](https://mlsanity.com/paper/2502.19308/) (2025)
- [Multi-Task Learning for Budbreak Prediction](https://mlsanity.com/paper/2301.01815/) (2023)
- [Analyzing the impact of forecast errors in the planning of wine grape harvesting operations using a multi-stage stochastic model approach](https://mlsanity.com/paper/2405.19997/) (2024)

## Abstract (arXiv)

Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions, they lack the precision required for site-specific management. Deep learning methods are a compelling alternative, but can produce biologically unrealistic predictions and require large-scale data. We propose a hybrid modeling approach that uses a neural network to parameterize a differentiable biophysical model and leverages multi-task learning for efficient data sharing across crop cultivars in data limited settings. By predicting the parameters of the biophysical model, our approach improves the prediction accuracy while preserving biological realism. Empirical evaluation using real-world and synthetic datasets demonstrates that our method improves prediction accuracy by 60% for phenology and 40% for cold hardiness compared to deployed biophysical models.