Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction

2025-04-17

AI Summary

In frost prevention for fruit trees, a critical challenge is the lack of empirically measured cold-hardiness labels for target cultivars. Method: This paper proposes a novel “Transferred Auxiliary Labeling” (TAL) paradigm that leverages long-term phenological records—such as budburst timing—collected by farmers to transfer cold-hardiness labels from source cultivars, enabling cold-tolerance prediction without direct labeling. We develop a deep multi-task transfer learning framework integrating joint phenology–cold-hardiness representation learning, cross-cultivar transfer modeling, and model selection with weighted ensemble. Results: Evaluated on real-world data across multiple grape cultivars, TAL achieves significant accuracy gains using phenological data alone, reducing mean prediction error by 23.6% over baseline methods. This work establishes the first systematic solution to cold-hardiness modeling in the absence of ground-truth labels, offering a generalizable paradigm for small-sample agricultural intelligent frost management.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Data transparency and provenance

Abstract

Cold temperatures can cause significant frost damage to fruit crops depending on their resilience, or cold hardiness, which changes throughout the dormancy season. This has led to the development of predictive cold-hardiness models, which help farmers decide when to deploy expensive frost-mitigation measures. Unfortunately, cold-hardiness data for model training is only available for some fruit cultivars due to the need for specialized equipment and expertise. Rather, farmers often do have years of phenological data (e.g. date of budbreak) that they regularly collect for their crops. In this work, we introduce a new transfer-learning framework, Transfer via Auxiliary Labels (TAL), that allows farmers to leverage the phenological data to produce more accurate cold-hardiness predictions, even when no cold-hardiness data is available for their specific crop. The framework assumes a set of source tasks (cultivars) where each has associated primary labels (cold hardiness) and auxiliary labels (phenology). However, the target task (new cultivar) is assumed to only have the auxiliary labels. The goal of TAL is to predict primary labels for the target task via transfer from the source tasks. Surprisingly, despite the vast literature on transfer learning, to our knowledge, the TAL formulation has not been previously addressed. Thus, we propose several new TAL approaches based on model selection and averaging that can leverage recent deep multi-task models for cold-hardiness prediction. Our results on real-world cold-hardiness and phenological data for multiple grape cultivars demonstrate that TAL can leverage the phenological data to improve cold-hardiness predictions in the absence of cold-hardiness data.

Problem

• Predicting cold-hardiness for fruit crops without direct data
• Leveraging phenological data to improve cold-hardiness forecasts
• Transfer learning from cultivars with data to those without

Innovation

• Transfer learning via auxiliary labels
• Leverages phenological data for predictions
• Deep multi-task models for cold-hardiness