Calibrating Biophysical Models for Grape Phenology Prediction via Multi-Task Learning

2025-08-05

AI Summary

Accurate prediction of grape phenology is critical for precision horticultural management (e.g., irrigation, fertilization), yet traditional biophysical models suffer from limited accuracy, while purely data-driven deep learning approaches are hindered by scarcity of variety-specific phenological observations. To address this, we propose a hybrid framework integrating multi-task learning with a differentiable biophysical model: a recurrent neural network parameterizes the biophysical model to enable cross-variety knowledge transfer; simultaneously, phenological stages, cold hardiness, and yield are jointly inverted under biologically grounded constraints. Experiments on both real-world and synthetic datasets demonstrate that our method significantly outperforms conventional biophysical models and end-to-end deep learning baselines—particularly in low-data regimes—achieving superior robustness and accuracy. This work establishes a novel paradigm for intelligent crop management under data-scarce conditions.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate prediction of grape phenology is essential for timely vineyard management decisions, such as scheduling irrigation and fertilization, to maximize crop yield and quality. While traditional biophysical models calibrated on historical field data can be used for season-long predictions, they lack the precision required for fine-grained vineyard management. Deep learning methods are a compelling alternative but their performance is hindered by sparse phenology datasets, particularly at the cultivar level. We propose a hybrid modeling approach that combines multi-task learning with a recurrent neural network to parameterize a differentiable biophysical model. By using multi-task learning to predict the parameters of the biophysical model, our approach enables shared learning across cultivars while preserving biological structure, thereby improving the robustness and accuracy of predictions. Empirical evaluation using real-world and synthetic datasets demonstrates that our method significantly outperforms both conventional biophysical models and baseline deep learning approaches in predicting phenological stages, as well as other crop state variables such as cold-hardiness and wheat yield.

Problem

• Improving grape phenology prediction accuracy for vineyard management
• Overcoming sparse phenology datasets in deep learning methods
• Combining multi-task learning with biophysical models for robustness

Innovation

• Hybrid modeling combining multi-task learning
• Recurrent neural network for biophysical model
• Shared learning across cultivars improves accuracy