Chunli Lv’s research while affiliated with China Agricultural University and other places

A deep reinforcement learning framework is presented for strategy generation and profit forecasting based on large-scale economic behavior data. By integrating perturbation-based augmentation, backward return estimation, and policy-stabilization mechanisms, the framework facilitates robust modeling and optimization of complex, dynamic behavior sequences. Experimental evaluations on four distinct behavior data subsets indicate that the proposed method achieved consistent performance improvements over representative baseline models across key metrics, including total profit gain, average reward, policy stability, and profit–price correlation. On the sales feedback dataset, the framework achieved a total profit gain of 0.37, an average reward of 4.85, a low-action standard deviation of 0.37, and a correlation score of R2=0.91. In the overall benchmark comparison, the model attained a precision of 0.92 and a recall of 0.89, reflecting reliable strategy response and predictive consistency. These results suggest that the proposed method is capable of effectively handling decision-making scenarios involving sparse feedback, heterogeneous behavior, and temporal volatility, with demonstrable generalization potential and practical relevance.

With the development of artificial intelligence and intelligent sensor technologies, traditional legal English teaching approaches have faced numerous challenges in handling multimodal inputs and complex reasoning tasks. In response to these challenges, a cross-modal legal English question-answering system based on visual and acoustic sensor inputs was proposed, integrating image, text, and speech information and adopting a unified vision–language–speech encoding mechanism coupled with dynamic attention modeling to effectively enhance learners’ understanding and expressive abilities in legal contexts. The system exhibited superior performance across multiple experimental evaluations. In the assessment of question-answering accuracy, the proposed method achieved the best results across BLEU, ROUGE, Precision, Recall, and Accuracy, with an Accuracy of 0.87, Precision of 0.88, and Recall of 0.85, clearly outperforming the traditional ASR+SVM classifier, image-retrieval-based QA model, and unimodal BERT QA system. In the analysis of multimodal matching performance, the proposed method achieved optimal results in Matching Accuracy, Recall@1, Recall@5, and MRR, with a Matching Accuracy of 0.85, surpassing mainstream cross-modal models such as VisualBERT, LXMERT, and CLIP. The user study further verified the system’s practical effectiveness in real teaching environments, with learners’ understanding improvement reaching 0.78, expression improvement reaching 0.75, and satisfaction score reaching 0.88, significantly outperforming traditional teaching methods and unimodal systems. The experimental results fully demonstrate that the proposed cross-modal legal English question-answering system not only exhibits significant advantages in multimodal feature alignment and deep reasoning modeling but also shows substantial potential in enhancing learners’ comprehensive capabilities and learning experiences.

To address the challenges of label sparsity and feature incompleteness in structured data, a self-supervised representation learning method based on multi-view consistency constraints is proposed in this paper. Robust modeling of high-dimensional sparse tabular data is achieved through integration of a view-disentangled encoder, intra- and cross-view contrastive mechanisms, and a joint loss optimization module. The proposed method incorporates feature clustering-based view partitioning, multi-view consistency alignment, and masked reconstruction mechanisms, thereby enhancing the model’s representational capacity and generalization performance under weak supervision. Across multiple experiments conducted on four types of datasets, including user rating data, platform activity logs, and financial transactions, the proposed approach maintains superior performance even under extreme conditions of up to 40% feature missingness and only 10% label availability. The model achieves an accuracy of 0.87, F1-score of 0.83, and AUC of 0.90 while reducing the normalized mean squared error to 0.066. These results significantly outperform mainstream baseline models such as XGBoost, TabTransformer, and VIME, demonstrating the proposed method’s robustness and broad applicability across diverse real-world tasks. The findings suggest that the proposed method offers an efficient and reliable paradigm for modeling sparse structured data.

One of the world’s most important economic crops, apples face numerous disease threats during their production process, posing significant challenges to orchard management and yield quality. To address the impact of complex disease characteristics and diverse environmental factors on detection accuracy, this study proposes a multimodal parallel transformer-based approach for apple disease detection and classification. By integrating multimodal data fusion and lightweight optimization techniques, the proposed method significantly enhances detection accuracy and robustness. Experimental results demonstrate that the method achieves an accuracy of 96%, precision of 97%, and recall of 94% in disease classification tasks. In severity classification, the model achieves a maximum accuracy of 94% for apple scab classification. Furthermore, the continuous frame diffusion generation module enhances the global representation of disease regions through high-dimensional feature modeling, with generated feature distributions closely aligning with real distributions. Additionally, by employing lightweight optimization techniques, the model is successfully deployed on mobile devices, achieving a frame rate of 46 FPS for efficient real-time detection. This research provides an efficient and accurate solution for orchard disease monitoring and lays a foundation for the advancement of intelligent agricultural technologies.

A deep learning-based tree pruning evaluation system is proposed in this study, which integrates hyperspectral images, sensor data, and expert system rules. The system aims to enhance the accuracy and robustness of tree pruning tasks through multimodal data fusion and online learning strategies. Various models, including Mask R-CNN, SegNet, Tiny-Segformer, Box2Mask, CS-Net, SVM, MLP, and Random Forest, were used in the experiments to perform tree segmentation and pruning evaluation, with comprehensive performance assessments conducted. The experimental results demonstrate that the proposed model excels in the tree segmentation task, achieving a precision of 0.94, recall of 0.90, F1 score of 0.92, and mAP@50 and mAP@75 of 0.91 and 0.90, respectively, outperforming other comparative models. These results confirm the effectiveness of multimodal data fusion and dynamic optimization strategies in improving the accuracy of tree pruning evaluation. The experiments also highlight the critical role of sensor data in pruning evaluation, particularly when combined with the online learning strategy, as the model can progressively optimize pruning decisions and adapt to environmental changes. Through this work, the potential and prospects of the deep learning-based tree pruning evaluation system in practical applications are demonstrated.

Timely and accurate detection of agricultural disasters is crucial for ensuring food security and enhancing post-disaster response efficiency. This paper proposes a deployable UAV-based multimodal agricultural disaster detection framework that integrates multispectral and RGB imagery to simultaneously capture the spectral responses and spatial structural features of affected crop regions. To this end, we design an innovative stride–cross-attention mechanism, in which stride attention is utilized for efficient spatial feature extraction, while cross-attention facilitates semantic fusion between heterogeneous modalities. The experimental data were collected from representative wheat and maize fields in Inner Mongolia, using UAVs equipped with synchronized multispectral (red, green, blue, red edge, near-infrared) and high-resolution RGB sensors. Through a combination of image preprocessing, geometric correction, and various augmentation strategies (e.g., MixUp, CutMix, GridMask, RandAugment), the quality and diversity of the training samples were significantly enhanced. The model trained on the constructed dataset achieved an accuracy of 93.2%, an F1 score of 92.7%, a precision of 93.5%, and a recall of 92.4%, substantially outperforming mainstream models such as ResNet50, EfficientNet-B0, and ViT across multiple evaluation metrics. Ablation studies further validated the critical role of the stride attention and cross-attention modules in performance improvement. This study demonstrates that the integration of lightweight attention mechanisms with multimodal UAV remote sensing imagery enables efficient, accurate, and scalable agricultural disaster detection under complex field conditions.

A hyperspectral maize nitrogen content prediction model is proposed, integrating a dynamic spectral–spatiotemporal attention mechanism with a graph neural network, with the aim of enhancing the accuracy and stability of nitrogen estimation. Across multiple experiments, the proposed method achieved outstanding performance on the test set, with R2=0.93, RMSE of 0.35, and MAE of 0.48, significantly outperforming comparative models including SVM, RF, ResNet, and ViT. In experiments conducted across different growth stages, the best performance was observed during the grain-filling stage, where R2 reached 0.96. In terms of accuracy, recall, and precision, the proposed model exhibited an average improvement exceeding 15%, demonstrating strong adaptability to temporal variation and generalization across spatial conditions. These results provide robust technical support for large-scale, nondestructive nitrogen monitoring in agricultural applications.

With the increase in trading frequency and the growing complexity of data structures, traditional quantitative strategies have gradually encountered bottlenecks in modeling capacity, real-time responsiveness, and multi-dimensional information integration. To address these limitations, a high-frequency signal generation framework is proposed, which integrates graph neural networks, cross-scale Transformer architectures, and macro factor modeling. This framework enables unified modeling of structural dependencies, temporal fluctuations, and macroeconomic disturbances. In predictive validation experiments, the framework achieved a precision of 92.4%, a recall of 91.6%, and an F1-score of 92.0% on classification tasks. For regression tasks, the mean squared error (MSE) and mean absolute error (MAE) were reduced to 1.76×10−4 and 0.96×10−2, respectively. These results significantly outperformed several mainstream models, including LSTM, FinBERT, and StockGCN, demonstrating superior stability and practical applicability.

Extreme weather prediction plays a crucial role in agricultural production and disaster prevention. This study proposes a lightweight extreme weather early warning model based on UAV cruise monitoring, a density-aware attention mechanism, and edge computing. Reinforcement learning is utilized to optimize UAV cruise paths, while a Transformer-based model is employed for weather prediction. Experimental results demonstrate that the proposed method achieves an overall prediction accuracy of 0.91, a precision of 0.93, a recall of 0.88, and an F1-score of 0.91. In the prediction of different extreme weather events, the proposed method attains an accuracy of 0.89 for strong wind conditions, 0.92 for hail, and 0.89 for late spring cold, all outperforming state-of-the-art methods. These results validate the effectiveness and applicability of the proposed approach in extreme weather forecasting.