Financial markets are increasingly shaped by heterogeneous information sources, including trading behaviors, order-book dynamics, textual events, investor sentiment, and macroeconomic conditions. These signals are often asynchronous, noisy, partially missing, and associated with different degrees of reliability, which makes trustworthy financial risk early warning challenging. To address these issues, this study proposes an uncertainty-aware multimodal financial sensing network, termed UAMF-Net. The model treats price series, trading volume, order books, news texts, investor sentiment, and macroeconomic variables as financial sensing signals and integrates them through three task-oriented modules. First, the asynchronous multimodal temporal alignment module uses absolute time encoding, relative interval modeling, event-lag representation, temporal gating, and target-time-guided cross-scale attention to align cross-frequency financial signals according to their relevance to the prediction time. Second, the risk-aware multimodal soft fusion module estimates modality-level risk contribution and signal reliability by combining fuzzy risk membership, modality confidence weights, and cross-modal consistency constraints. Third, the uncertainty-aware risk early warning module adopts evidential learning to generate nonnegative class evidence, derive risk-category probabilities from Dirichlet parameters, estimate predictive uncertainty from total evidence strength, and jointly predict continuous risk intensity. Experimental results show that UAMF-Net achieves the best overall performance, with Accuracy, Precision, Recall, F1-score, Macro-F1, ROC-AUC, and PR-AUC reaching $0.882$, $0.864$, $0.849$, $0.856$, $0.839$, $0.941$, and $0.824$, respectively, while ECE and Brier score are reduced to $0.037$ and $0.096$. Under severe temporal asynchrony, UAMF-Net maintains an Accuracy of $0.849$, a Macro-F1 of $0.797$, and a PR-AUC of $0.774$. Under the missing multiple modalities setting, it achieves an Accuracy of $0.842$ and a Macro-F1 of $0.788$. The uncertainty analysis further shows that Risk Precision@90% reaches $0.889$. Validation on FNSPID, Daily News, and StockEmotions also confirms its generalization ability across public financial benchmarks. These results indicate that UAMF-Net improves financial risk early warning by jointly modeling temporal asynchrony, modality reliability, and predictive uncertainty.

Smart greenhouse management requires decisions that balance yields, costs, resource use, emissions, and revenue. Existing threshold-based controls and many learning models use sensor data mainly for environmental feedback or yield prediction, leaving limited support for operational decisions tied to farm profitability and resource efficiency. This study proposes AER-SFRDNet, a multisource sensor fusion and deep reinforcement learning framework for revenue-aware greenhouse regulation. Crop images, environmental sensor sequences, management records, resource consumption data, and economic variables from facility tomato production are integrated into a unified agro-economic state representation. A yield–cost–revenue prediction module estimates the yield, resource consumption, production cost, sales revenue, and net revenue. These outputs guide a reinforcement learning module that optimizes continuous actions including irrigation, ventilation, supplemental lighting, heating, and CO₂ application. A comparison with traditional machine learning, unimodal deep learning, multimodal learning, and reinforcement learning baselines shows lower prediction errors and improved regulation outcomes. AER-SFRDNet achieves a yield MAE of $0.486$, yield RMSE of $0.674$, yield $R^2$ of $0.893$, cost RMSE of $0.613$, and net revenue MAPE of $10.82$. In regulation experiments, the cumulative net revenue and input–output ratio reach $1.276$ and $1.263$, while the normalized energy use, water use, and carbon emissions per unit yield decrease to $0.752$, $0.781$, and $0.741$, respectively. The results suggest that agro-economic state modeling can support greenhouse decisions that consider both revenue and sustainability-related resource efficiency.