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

• URL: http://arxiv.org/abs/2508.03898v1
• Date: Tue, 05 Aug 2025 20:36:11 GMT
• Title: Calibrating Biophysical Models for Grape Phenology Prediction via Multi-Task Learning
• Authors: William Solow, Sandhya Saisubramanian,
• Abstract summary: We propose a hybrid modeling approach that combines multi-task learning with a recurrent neural network to parameterize a differentiable biophysical model.<n>By using multi-task learning to predict the parameters of the biophysical model, our approach enables shared learning across cultivars while preserving biological structure.
• Score: 5.796482272333648
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy [6.537907917185119]
  We present a phenology model describing dormancy in fruit trees, integrating biophysical models with a neural network to address structural disparities.<n>Our approach consistently outperforms both traditional biophysical and machine learning models in predicting blooming dates across years.
  arXiv  Detail & Related papers  (2025-01-28T10:41:48Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Deep learning meets tree phenology modeling: PhenoFormer vs. process-based models [3.864610688022995]
  PhenoFormer is a neural architecture better suited than traditional statistical methods at predicting phenology under shift in climate data distribution. Our numerical experiments on a 70-year dataset of 70,000 phenological observations from 9 woody species in Switzerland show that PhenoFormer outperforms traditional machine learning methods.
  arXiv  Detail & Related papers  (2024-10-30T15:40:55Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Improving Biomedical Entity Linking with Retrieval-enhanced Learning [53.24726622142558]
  $k$NN-BioEL provides a BioEL model with the ability to reference similar instances from the entire training corpus as clues for prediction. We show that $k$NN-BioEL outperforms state-of-the-art baselines on several datasets.
  arXiv  Detail & Related papers  (2023-12-15T14:04:23Z)
• Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions [0.276240219662896]
  Predicting the bioactivity of a ligand is one of the hardest and most important challenges in computer-aided drug discovery. Despite years of data collection and curation efforts, bioactivity data remains sparse and heterogeneous. We present a hierarchical meta-learning framework that exploits the information synergy across disparate assays.
  arXiv  Detail & Related papers  (2023-08-17T16:26:58Z)
• Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome Data [0.2812395851874055]
  We provide the first deep investigation of the predictive potential of machine learning models to understand the connections between soil and biological phenotypes. We show that prediction is improved when incorporating environmental features like soil physicochemical properties and microbial population density into the models.
  arXiv  Detail & Related papers  (2023-06-19T20:52:37Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples [52.549928980694695]
  In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
  arXiv  Detail & Related papers  (2020-10-20T06:06:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.