An Interpretable Stacked Ensemble Learning Framework for Wheat Storage Quality Prediction

Xinze Li, Wenyue Wang, Bing Pan, Siyu Zhu, Junhui Zhang, Yunzhao Ma, Hongpeng Guo, Zhe Liu, Wenfu Wu and Yan Xu ()
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Xinze Li: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Wenyue Wang: Institute of Xinjiang Uygur Autonomous Region Grain and Oil Science (Grain and Oil Product Quality Supervision and Inspection Station of Xinjiang Uygur Autonomous Region), Urumqi 830000, China
Bing Pan: Institute of Xinjiang Uygur Autonomous Region Grain and Oil Science (Grain and Oil Product Quality Supervision and Inspection Station of Xinjiang Uygur Autonomous Region), Urumqi 830000, China
Siyu Zhu: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Junhui Zhang: Institute of Xinjiang Uygur Autonomous Region Grain and Oil Science (Grain and Oil Product Quality Supervision and Inspection Station of Xinjiang Uygur Autonomous Region), Urumqi 830000, China
Yunzhao Ma: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Hongpeng Guo: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Zhe Liu: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Wenfu Wu: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Yan Xu: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China

Agriculture, 2025, vol. 15, issue 17, 1-21

Abstract: Accurate prediction of wheat storage quality is essential for ensuring storage safety and providing early warnings of quality deterioration. However, existing methods focus solely on storage environmental conditions, neglecting the spatial distribution of temperature within grain piles, lacking interpretability, and generally failing to provide reliable forecasts of future quality changes. To overcome these challenges, an interpretable prediction framework for wheat storage quality based on stacked ensemble learning is proposed. Three key features, Effective Accumulated Temperature (EAT), Cumulative High Temperature Deviation (CHTD), and Cumulative Temperature Gradient (CTG), were derived from grain temperature data to capture the spatiotemporal dynamics of the internal temperature field. These features were then input into the stacked ensemble learning model to accurately predict historical quality changes. In addition, future grain temperatures were predicted with high precision using a Graph Convolutional Network-Temporal Fusion Transformer (GCN-TFT) model. The temperature prediction results were then employed to construct features and were fed into the stacked ensemble learning model to enable future quality change prediction. Baseline experiments indicated that the stacked model significantly outperformed individual models, achieving R 2 = 0.94, MAE = 0.44 mg KOH/100 g, and RMSE = 0.59 mg KOH/100 g. SHAP interpretability analysis revealed that EAT constituted the primary driver of wheat quality deterioration, followed by CHTD and CTG. Moreover, in future quality prediction experiments, the GCN-TFT model demonstrated high accuracy in 60-day grain temperature forecasts, and although the prediction accuracy of fatty acid value changes based on features derived from predicted temperatures slightly declined compared to features based on actual temperature data, it remained within an acceptable precision range, achieving an MAE of 0.28 mg KOH/100 g and an RMSE of 0.33 mg KOH/100 g. The experiments validated that the overall technical route from grain temperature prediction to quality prediction exhibited good accuracy and feasibility, providing an efficient, stable, and interpretable quality monitoring and early warning tool for grain storage management, which assists managers in making scientific decisions and interventions to ensure storage safety.

Keywords: food security; grain storage; stacked ensemble learning; quality prediction; interpretability analysis; temperature forecasting (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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