Citation :

Yedukondalu Gamidelli, Mousmi Ajay Chaurasia, S Nallusamy, "XCalibYieldAI: A Dual-Stage Explainable and Calibrated Deep Learning Framework for Crop Yield Forecasting," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 7, pp. 547-583, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I7P132

Abstract

Crop yield forecasting is an essential approach for providing accurate and interpretable information to facilitate food security, resource allocation, and Policy-Making in agriculture. Limitations in capacity for regional calibration, transparency, and generalisation are common issues for many traditional statistical methods and Black-Box deep learning models, hindering proper adaptability across heterogeneous Agro-Climatic zones. In addition, the predictions of most current models are not interpretable, therefore less trustworthy and unusable in practice. To fill these gaps, we proposed a Two-Stage deep learning framework, called XCalibYieldAI, which aims to maximise both prediction accuracy and interpretability for crop yield. The proposed method combines attention-based temporal modelling with Region-Specific calibration and explainable AI methods, including SHAP value analysis, temporal attention visualisation, and geospatial overlays. They collectively maximise spectral, atmospheric, and vegetation information while also accommodating regional differences in crop phenology and climate. Multi-season remote sensing datasets, with a case study over the continental USA, show that the proposed framework outperforms state-of-the-art CNN, LSTM, and Transformer baselines, resulting in statistically significant improvements in macro accuracy (up to 92.4%) and R² scores. This enhances stakeholder confidence by providing actionable visual explainability through measures of the temporal importance of crop growth stages and the spatial heterogeneity of yield across yield zones. This new XCalibYieldAI framework addresses the critical Trade-Off between crop yield models that are scalable across large areas and highly generalisable, and those that enable complex deep learning predictions to drive agronomic decisions. This acts as an accessible, Open-Source, and Data-Driven platform for contemporary precision agriculture use cases.

Keywords

Crop Yield Forecasting, Explainable AI, Temporal Attention, Regional Calibration, Remote Sensing Data.

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