Explainable Deep Learning for Greenhouse Horticulture: Feature and Temporal Interpretability in Crop Yield and Energy Optimization

Optimizing crop yield while minimizing energy consumption remains a central challenge in greenhouse horticulture. This study introduces an integrated deep learning framework that couples multi-horizon time-series forecasting with dual-layered explainability to address the critical need for spatiotemporal transparency in optimizing greenhouse crop yield and energy efficiency. Four deep learning architectures, including the One-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory Network (BiLSTM), and TinyTimeMixer (TTM), were evaluated across two varieties of capsicum. LSTM and BiLSTM achieved the highest accuracy for incremental yield prediction, whereas TTM outperformed other models in forecasting daily energy usage, reflecting the distinct temporal characteristics of biological growth and environment-driven energy demand. To uncover the factors driving these predictions, two complementary explainability methods were applied: Gradient SHapley Additive exPlanations (SHAP) for feature-level attribution and a Temporal Convolutional Network with Convolutional Block Attention Module (TCN–CBAM) attention mechanism for joint temporal-feature interpretation. Radiation and drainage-related variables consistently emerged as the dominant contributors to yield, whereas external temperature, and humidity were the primary determinants of energy usage. Temporal attention further showed that yield is influenced by both recent irrigation responses and longer-term developmental dynamics, while energy consumption is driven mainly by short-term climatic fluctuations. These findings provide actionable insights for irrigation scheduling, climate-control strategies, and energy optimization, supporting more transparent and sustainable greenhouse management.