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

Abstract

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 \emph{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 \emph{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.

Figures (13)

• Figure 1: Overview of our proposed method using phenology prediction as an example. In this case, seasonal phenological stages guide vineyard management operations. Our Dynamic Model Calibration via Multi-Task Learning (DMC-MTL) approach uses a pretrained neural network and biophsyical model to produce high fidelity and biologically realistic phenology state forecasts. The pretrained RNN produces daily parameter predictions (Base Temperature, Temperature Sum for Bud Break, etc.) of the Growing Degree Day (GDD) phenology model which handles the daily stage prediction.
• Figure 2: Network architecture of our approach DMC-MTL. The multi-task RNN ($\mathcal{F}_\theta$) sequentially embeds cultivar i.d. $i$ and concatenates it with the daily weather features $W_t$ to predict a parameterization $\omega_t$ of the biophysical model $\mathcal{M}$. Using the weather input to the biophysical model $W_t'$ and the daily parameterization $\omega_t$, crop state forecasts $Y_t'\ldots Y_{t+k}'$ can be generated.
• Figure 3: In-Season Adaptation with DMC-MTL. Cultivar id $i$ and error between observed and DMC-MTL predicted crop states are passed to the EEN. The parameters predicted by the EEN are combined additively to the prediction made by DMC-MTL's pretrained RNN before parameterizing the biophyiscal model.
• Figure 4: DMC-MTL, Classification, and Regression model predictions for (a) grape phenology and (b) grape cold hardiness. DMC-MTL makes biologically realistic predictions while deep learning model predictions do not always respect biologicaly laws.
• Figure 5: The performance of DMC-MTL models compared to deep learning and biophysical models under limited per-cultivar training data for grape phenology (Pheno) and cold hardiness (CH). Results are averaged over five seeds using the same two-seasons-per-cultivar evaluation sets.