# PHTFNet-RPM: a probabilistic hybrid network with RPM for tobacco root disease forecasting

## TL;DR

A new model called PHTFNet-RPM is developed to predict tobacco root diseases more accurately by combining weather data and disease metrics with uncertainty estimates.

## Contribution

The novel PHTFNet-RPM model integrates a hybrid input structure with RPM and probabilistic uncertainty quantification for improved disease forecasting.

## Key findings

- PHTFNet-RPM achieved 4.44%–16.43% lower MAE than existing models in forecasting tobacco root diseases.
- The model reliably forecasts disease progression trends using historical weather observations alone.
- Uncertainty quantification enhances prediction reliability and supports practical disease management.

## Abstract

Tobacco growers usually face particular challenges in predicting the risks of tobacco root diseases due to complex pathogenesis, concealed early symptoms, and heterogeneous farm conditions.

To address this problem, we proposed a flexible Probabilistic Hybrid Temporal Fusion Network with Random Period Mask (PHTFNet-RPM). This model is designed to forecast future multi-day disease incidences and indices. It incorporates a hybrid input structure with RPM to handle configurable static management variables and time-series data of weather factors and disease metrics, using the RPM to simulate diverse absences of historical observations. The model's internal hierarchically aggregated modules learn cross-variable and cross-temporal feature representations to model the complex non-linear relationships. Furthermore, probabilistic theory-based uncertainty quantification is designed to enhance the model's credibility and reliability.

The proposed PHTFNet-RPM was validated using a large-scale time-series dataset of tobacco root diseases, organized from 20-year meteorological and disease survey records in Chuxiong Prefecture, Yunnan Province. Extensive comparative experiments demonstrated that our model achieves a 4.44%–16.43% lower mean absolute error (MAE) than existing models (including LR, SVR, CNN-LSTM, and LSTM-Attention).

The results confirm that the model can reliably forecast disease progression trends under different configurations, even when relying solely on historical weather observations. The integration of uncertainty quantification provides a robust tool for assessing prediction reliability, offering significant practical value for disease management.