This paper presents an efficient weed detection method based on the latent diffusion transformer, aimed at enhancing the accuracy and applicability of agricultural image analysis. The experimental results demonstrate that the proposed model achieves a precision of 0.92, a recall of 0.89, an accuracy of 0.91, a mean average precision (mAP) of 0.91, and an F1 score of 0.90, indicating its outstanding performance in complex scenarios. Additionally, ablation experiments reveal that the latent-space-based diffusion subnetwork outperforms traditional models, such as the the residual diffusion network, which has a precision of only 0.75. By combining latent space feature extraction with self-attention mechanisms, the constructed lightweight model can respond quickly on mobile devices, showcasing the significant potential of deep learning technologies in agricultural applications. Future research will focus on data diversity and model interpretability to further enhance the model’s adaptability and user trust.

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.

Data privacy protection is increasingly critical in fields like healthcare and finance, yet existing methods, such as Fully Homomorphic Encryption (FHE), differential privacy (DP), and federated learning (FL), face limitations like high computational complexity, noise interference, and communication overhead. This paper proposes a novel data obfuscation method based on probability density and information entropy, leveraging a probability density extraction module for global data distribution modeling and an information entropy fusion module for dynamically adjusting the obfuscation intensity. In medical image classification, the method achieved precision, recall, and accuracy of 0.93, 0.89, and 0.91, respectively, with a throughput of 57 FPS, significantly outperforming FHE (0.82, 23 FPS) and DP (0.84, 25 FPS). Similarly, in financial prediction tasks, it achieved precision, recall, and accuracy of 0.95, 0.91, and 0.93, with a throughput of 54 FPS, surpassing traditional approaches. These results highlight the method’s ability to balance privacy protection and task performance effectively, offering a robust solution for advancing privacy-preserving technologies.