Explainable deep learning for predicting temperature breaks and identifying their causal drivers in fresh produce cold chains

This study proposes an integrated framework combining deep learning, causal discovery, explainability, and domain thresholds for proactive monitoring in temperature-sensitive fresh fruits and vegetables cold chains, a domain that remains largely unexplored. The framework predicts temperature breaks (a critical physical hazard that compromises food safety and accelerates postharvest losses), prospectively identifies their causal drivers, and prioritizes countermeasures.

Using two IoT-gathered datasets from an Apple cold room in South Africa, eight machine learning models and three baselines were compared for temperature prediction, including a proposed LSTM-GRU hybrid (LSGR) model. Causal relationships between measured parameters and predicted temperature were identified using Latent Peter & Clark Momentary Conditional Independence (LPCMCI; compared with PCMCI). At the same time, SHapley Additive exPlanations quantified feature importance, and domain-based thresholds complemented them for countermeasure prioritization after temperature-break prediction (TBP).

On the extended dataset, deep neural networks' (DNN) R ² and normalized mean squared error improved by up to 30% and to 61%, respectively, over the smaller dataset, demonstrating strong generalizability to substantially different cold room operating conditions. The LSGR demonstrated the best overall performance across both temperature and TBP tasks among the recurrent models assessed, raising the fewest false alarms per day. All DNNs flagged breaks in advance; no baseline did. LPCMCI recovered most direct causal links after break prediction, especially for lagged temperature, identifying it as the dominant factor associated with predicted breaks. Notably, post-TBP, causality, importance, and thresholds strongly agreed on the same top two critical parameters or countermeasures (temperature and humidity), providing consistent and actionable insights for decision-making.

These findings demonstrate the viability of combining causal discovery, explainability and thresholds for cold chain management, with implications for reducing food wastage and enhancing quality, safety, and sustainability from farm to fork in temperature-sensitive FFVs supply chains. LPCMCI recovered a few links overall, indicating the influence of unmeasured drivers and warranting further investigation, including additional features, their interactions, and seasonal data, as well as a fuller characterization of causality-regime dependence.