Attention-Based Transformer Framework with Predictive Uncertainty Quantification for Multi-Crop Yield Forecasting
Year: 2026
Type: article
Abstract: Accurate crop yield forecasting is essential for ensuring food security, optimizing agricultural resource allocation, and supporting climate-resilient farming systems. Recent advances in deep learning have improved yield prediction accuracy; however, most existing models provide deterministic estimates without quantifying predictive uncertainty. This limitation restricts their reliability under climatic variability, missing data, and real-world decision-making scenarios where risk awareness is critical. This study utilizes two publicly available multi-crop datasets comprising historical yield records integrated with weather and soil attributes across multiple growing seasons. An attention-based Transformer framework is proposed, augmented with uncertainty quantification through Monte Carlo Dropout, Quantile Regression, and Bayesian Attention mechanisms. The proposed approach represents an integrated uncertainty-aware Transformer framework that combines temporal self-attention with complementary uncertainty estimation strategies. The contribution of this work lies in the systematic integration and comparative evaluation of multiple uncertainty quantification mechanisms within a unified deep learning framework for multi-crop yield forecasting. Experimental results demonstrate improved predictive accuracy and calibration compared to deterministic baselines. However, these findings are bounded by the scope of the datasets, which consist of coarse tabular climatic and soil variables, and should be interpreted accordingly.
Source: Computation
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,172
Funder: Multimedia University
OpenAlex ID: W7154460281