To address the limitations of traditional computational fluid dynamics (CFD) simulations, such as high computational cost, long processing times, and limited scalability, this study identifies the inefficiencies of existing data‐driven prediction methods, which often lack spatial–temporal coordination mechanisms and fail to capture fine‐grained dynamic features of UAV airflow fields. We propose a novel deep learning model, VAN‐ConvLSTM, for rapid and accurate prediction of UAV downwash airflow. Unlike conventional ConvLSTM‐based frameworks, which struggle with modeling long‐range dependencies and detailed spatial variations, our model introduces a visual attention unit (VAN) to enhance spatiotemporal sensitivity. The model architecture combines a convolutional encoder for spatial feature extraction, a VAN module for attention‐guided temporal modeling, and a ConvLSTM decoder for sequence generation. This synergistic design improves both the accuracy and interpretability of airflow prediction. Experimental results show that the VAN‐ConvLSTM model achieves an SSIM score of 0.96, demonstrating high consistency with CFD simulations. Compared to baseline methods, our model reduces error while improving stability and spatial fidelity. Ablation studies further validate the individual contributions of VAN and ConvLSTM modules. The results, verified through three representative case studies, confirm that VAN‐ConvLSTM outperforms state‐of‐the‐art approaches across multiple evaluation metrics, while offering significantly enhanced computational efficiency. This demonstrates its strong potential as a reliable and scalable alternative to traditional CFD methods in rotor airflow prediction scenarios.

Rice leaf disease (RLD) is one of the major factors that cause the decline in production, and the automatic recognition of such diseases under natural field conditions is of great significance for timely targeted rice management. Although many machine learning approaches have been proposed for RLD recognition, scale variation is still a challenging problem that affects prediction accuracy, especially in uncontrolled environments, such as natural fields. Also, the existing RLD data sets are collected in laboratory environments or with a constant scale, which cannot be used to develop the RLD classification algorithms under natural field conditions. To tackle these particular challenges, we propose a multiscale voting mechanism for RLD recognition under natural field conditions. First, data from 26 rice fields were collected to build a data set containing 6046 images of RLD. Afterwards, a feature pyramid was embedded into a mainstream classification architecture (EfficientNet) with a bottom-up and top-down pathway for feature fusion at different scales. To further reduce the inconsistency among multiscaled features, a multiscale voting strategy with regard to probability distribution was proposed to integrate the decisions from various scales. Each proposed module was carefully validated through an ablation study to demonstrate its effectiveness, and the proposed method was compared with a few state-of-the-art algorithms, including the Single Shot MultiBox Detector, Feature Pyramid Networks, Path Aggregation Network, and Bidirectional Feature Pyramid Network. Experimental results have shown that the classification accuracy of our model can reach 90.24%, which is 4.48% higher than that of the original EfficientNet-b0 model and 1.08% higher than that of existing multiscale networks. Finally, we exploit and demonstrate a visualized explanation for the boosted performance from the proposed model. As an extra outcome, our data set and codes are available at http://github.com/huanghsheng/multiscale-voting-mechanism to benefit the whole research community.