AI-driven weather downscaling for smart agriculture using autoencoders and transformers
Journal
Computers and Electronics in Agriculture
Journal Volume
232
Start Page
110129
ISSN
0168-1699
Date Issued
2025-05
Author(s)
Abstract
Artificial Intelligence (AI) is reshaping agriculture by driving smarter, data-driven practices that enhance regional weather forecasting and support proactive, informed decision-making. Advances in Big Data, IoT, Remote Sensing, and Machine Learning are accelerating this transformation, with Transformer architectures increasingly pivotal in refining agricultural management strategies, especially in Taiwan. In this study, we develop a hybrid Convolutional Autoencoder and LSTM-based Transformer Network (CAE-LSTMT) to downscale six-hour simulation data into precise hourly forecasts, validated using 55,538 temperature and relative humidity records (2020–2023) from Taiwan's Jhuoshuei River basin, provided by the Central Weather Administration (CWA). The model was trained (70 %), validated (10 %), and tested (20 %) to optimize its configuration and performance. This CAE-LSTMT model substantially enhances spatiotemporal weather forecast resolution, transforming six-hour regional data into hourly forecasts with improved accuracy. It yields temperature forecast gains of 5.66 % to 20.39 % and relative humidity improvements of 8.05 % to 12.76 %, with reduced forecast biases compared to traditional LSTM models. The model demonstrates exceptional accuracy in vapor pressure deficit (VPD) predictions, achieving mean absolute errors (MAE) between 0.15 to 0.21 kPa across regions and 0.16 to 0.20 kPa seasonally, significantly outperforming the CWA model. Accurate VPD forecasts allow farmers to manage irrigation and minimize crop stress, directly supporting plant health and yield optimization. For heat index classification, the model achieves up to 96 % ACCURACY, with mean absolute percentage errors (MAPE) of 4 % to 23 %, significantly exceeding the CWA model's ACCURACY range of 35 % to 79 % and MAPE of 29 % to 70 %. This high precision in heat index forecasting empowers farmers to protect crops and livestock against heat stress. By extracting critical features from high-dimensional data, the CAE-LSTMT model advances environmental downscaling for multi-site, multi-horizon weather data, showing significant promise for Smart Agriculture and Health Advisory Systems. This approach offers precise, actionable forecasts, optimizing agricultural practices and reducing climate-related risks, underscoring its impact on sustainable agricultural and environmental management.
Subjects
Deep learning
Smart agriculture
Transformer
Vapor pressure deficit (VPD)
Weather downscaling
Publisher
Elsevier BV
Description
Article number: 110129
Type
journal article