Exploring the Feasibility of Deep Learning Models for Long-term Disease Prediction: A Case Study for Wheat Yellow Rust in England

• URL: http://arxiv.org/abs/2501.15677v1
• Date: Sun, 26 Jan 2025 21:22:54 GMT
• Title: Exploring the Feasibility of Deep Learning Models for Long-term Disease Prediction: A Case Study for Wheat Yellow Rust in England
• Authors: Zhipeng Yuan, Yu Zhang, Gaoshan Bi, Po Yang,
• Abstract summary: This study explores the feasibility of using deep learning models to predict outbreaks of wheat yellow rust in British fields. We employ two deep learning models, including fully connected neural networks and long short-term memory. Preliminary results indicate that deep learning models can effectively capture the complex interactions between multiple factors influencing disease dynamics.
• Score: 6.760255060153002
• License:
• Abstract: Wheat yellow rust, caused by the fungus Puccinia striiformis, is a critical disease affecting wheat crops across Britain, leading to significant yield losses and economic consequences. Given the rapid environmental changes and the evolving virulence of pathogens, there is a growing need for innovative approaches to predict and manage such diseases over the long term. This study explores the feasibility of using deep learning models to predict outbreaks of wheat yellow rust in British fields, offering a proactive approach to disease management. We construct a yellow rust dataset with historial weather information and disease indicator acrossing multiple regions in England. We employ two poweful deep learning models, including fully connected neural networks and long short-term memory to develop predictive models capable of recognizing patterns and predicting future disease outbreaks.The models are trained and validated in a randomly sliced datasets. The performance of these models with different predictive time steps are evaluated based on their accuracy, precision, recall, and F1-score. Preliminary results indicate that deep learning models can effectively capture the complex interactions between multiple factors influencing disease dynamics, demonstrating a promising capacity to forecast wheat yellow rust with considerable accuracy. Specifically, the fully-connected neural network achieved 83.65% accuracy in a disease prediction task with 6 month predictive time step setup. These findings highlight the potential of deep learning to transform disease management strategies, enabling earlier and more precise interventions. Our study provides a methodological framework for employing deep learning in agricultural settings but also opens avenues for future research to enhance the robustness and applicability of predictive models in combating crop diseases globally.

Related papers

• Whole-Genome Phenotype Prediction with Machine Learning: Open Problems in Bacterial Genomics [0.8437187555622164]
  We set up problems surrounding phenotype prediction from bacterial whole-genome datasets and extend those to learning causal effects. We discuss challenges that impact the reliability of a machine's decision-making when faced with datasets of this nature.
  arXiv  Detail & Related papers  (2025-02-11T18:25:14Z)
• Early Detection of Coffee Leaf Rust Through Convolutional Neural Networks Trained on Low-Resolution Images [27.195033353775006]
  Coffee leaf rust, a foliar disease caused by the fungus Hemileia vastatrix, poses a major threat to coffee production. Deep learning models for enhancing early disease detection require extensive processing power and large amounts of data. We propose a preprocessing technique that involves convolving training images with a high-pass filter to enhance lesion-leaf contrast.
  arXiv  Detail & Related papers  (2024-07-20T03:24:25Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Harmful algal bloom forecasting. A comparison between stream and batch learning [0.7067443325368975]
  Harmful Algal Blooms (HABs) pose risks to public health and the shellfish industry. This study develops a machine learning workflow for predicting the number of cells of a toxic dinoflagellate. The model DoME emerged as the most effective and interpretable predictor, outperforming the other algorithms.
  arXiv  Detail & Related papers  (2024-02-20T15:01:11Z)
• Deep learning methods for drug response prediction in cancer: predominant and emerging trends [50.281853616905416]
  Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
  arXiv  Detail & Related papers  (2022-11-18T03:26:31Z)
• Forecasting Black Sigatoka Infection Risks with Latent Neural ODEs [5.483149401688839]
  Black Sigatoka disease severely decreases global banana production. Farmers in developing countries face significant banana crop losses. We present MR. NODE, a neural network that models the dynamics of black Sigatoka infection.
  arXiv  Detail & Related papers  (2020-12-01T21:59:53Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19 [75.99038202534628]
  We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
  arXiv  Detail & Related papers  (2020-09-23T22:35:43Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.