Context-Aware Representation Learning in High-Dimensional Dynamic Systems

Abstract

Rapid advancements in remote sensing technologies and distributed monitoring systems have enabled the large-scale acquisition of high-dimensional geo-spatiotemporal data in agricultural and environmental domains. However, extracting meaningful representations remains challenging due to complex spatial–temporal dependencies and limited labeled data availability. To develop an accurate and robust framework for crop yield prediction by learning context-aware representations from high-dimensional dynamic agricultural data. Multi-source datasets are utilized, including satellite imagery, ground-based sensor measurements, and historical environmental records. Key variables include vegetation indices (NDVI, EVI), temperature, rainfall, soil moisture, humidity, solar radiation, and temporal growth indicators. Data preprocessing involves Z-score normalization to standardize feature distributions, followed by Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. The proposed model integrates Red Panda Optimized Variational Autoencoder with attention mechanism (RPO-VAE-ATT) to jointly capture spatial structures and long-range temporal dependencies.  The proposed RPO-VAE-ATT model uses VAE for extracting compact representations from high-dimensional data, RPO for optimizing parameters, and attention to capture temporal dependencies, enabling accurate learning of spatial–temporal patterns for robust and efficient crop yield prediction. The framework achieved high predictive performance with improved accuracy (95.3%), precision (95.03%), recall (96%), and F1-score (97.39%), demonstrating strong generalization and robustness under data-scarce conditions, and was implemented using Python tools. The framework provides efficient and adaptive representation learning for high-dimensional dynamic systems, enabling reliable crop yield prediction and supporting data-driven decision-making in precision agriculture.This is an open access article under CC BY 4.0, allowing unrestricted use with proper attribution, a license link, and indication of any changes made.