Accurate monitoring of substrate moisture content and electrical conductivity (EC) is essential for irrigation and nutrient management in soilless strawberry cultivation. However, conventional sensor-based approaches are limited in spatial coverage. This study developed a multimodal prediction framework integrating canopy multispectral imaging (713–920 nm) with greenhouse environmental and irrigation time-series data to estimate substrate state non-destructively. Four spectral preprocessing schemes were evaluated, and the first derivative of digital number values was adopted as the primary preprocessing condition. Five regression models, including a proposed spectrum-query cross-attention long short-term memory network (SpecAtten-LSTM), were compared across six spectral input configurations and five non-spectral baselines. Substrate EC was predicted accurately from either modality alone. Extreme gradient boosting reached R² = 0.9710, and the environment-only baselines achieved comparable performance, suggesting that either modality contained sufficient information for EC prediction. For substrate moisture content, the highest performance was obtained when spectral and environmental information were combined. SpecAtten-LSTM achieved the highest accuracy (R² = 0.7463) under the full multimodal configuration, and an ablation analysis confirmed that its cross-attention and fusion modules drove this gain. Permutation importance identified relative humidity as the dominant environmental variable for both targets. The results indicate that canopy-level observations can be used to estimate root-zone substrate conditions and that spectral information provides additional value primarily for substrate moisture content prediction.