A Spatiotemporal Generative AI Framework for Agronomic Forecasting Using Semi-Supervised Learning on Remote Sensing Time-Series

Agronomic forecasting, especially crop yield prediction, faces significant challenges due to data sparsity, irregular sampling, and the limited availability of labeled time-series data. To address these issues, this study proposes a Spatiotemporal Generative Adversarial Network (ST-GAN) framework designed to generate temporally consistent synthetic data for time-series forecasting tasks. We evaluate the performance of an LSTM regression model trained on both real-world and ST-GAN-generated datasets, focusing on predicting crop yield from synthetic features like temperature, rainfall, and pesticide usage. Experimental results show that training the LSTM model with ST-GANgenerated data yields substantial improvements in predictive accuracy, with a 29.3 % reduction in mean squared error (MSE), highlighting the model's ability to capture complex temporal dependencies effectively. Importantly, the synthetic sequences produced by ST-GAN maintain critical spatiotemporal coherence, allowing the model to generalize better to unseen real-world data, resulting in lower error rates on validation samples. The incorporation of semi-supervised learning within the ST-GAN framework further enhances its utility, enabling improved forecasting in low-resource environments where labeled data is scarce. By leveraging the strength of both synthetic data generation and semi-supervised learning, our approach overcomes the limitations of traditional methods, offering a robust solution for agronomic forecasting.