With the popularity of location-aware devices (e.g., smart phones), a large number of trajectory data were collected. The trajectory dataset can be used in many fields including traffic monitoring, market analysis, city management, etc. The collection and release of trajectory data will raise serious privacy concerns for users. If users’ privacy is not protected enough, they will refuse to share their trajectory data. In this paper, a new trajectory privacy protection method based on random sampling differential privacy (TPRSDP), which can provide more security protection, is proposed. Compared with other methods, it takes less time to run this method. Experiments are conducted on two real world datasets to validate the proposed scheme, and the results are compared with others in terms of running time and information loss. The performance of the scheme with different parameter values is verified. The setting of the new scheme parameters is discussed in detail, and some valuable suggestions are given.

Crop diseases and pests severely threaten global food security. While deep learning has shown promise in controlled settings, real-world field conditions—characterized by complex backgrounds, variable lesion scales, and high inter-class similarity—remain challenging. To overcome limitations in feature representation, scale adaptability, and model efficiency, we propose DPMFNet, a lightweight dual-path network integrating Spatial–Channel Dual-Attention (SCDA) and Multi-Scale Depthwise Separable Convolution (MDSC) modules. SCDA enhances critical regions via dynamic channel–spatial weighting with minimal overhead, while MDSC captures multi-scale contextual information through parallel dilated convolutions. Both are embedded into an improved residual block (AttMDSCBlock) to boost representational power while reducing parameters. A cross-attention mechanism fuses local details and global context from dual pathways, and a lightweight pyramid strategy adaptively integrates features across resolutions. Evaluated on PlantVillage and the AI Challenger 2018 dataset, DPMFNet achieves state-of-the-art accuracy among lightweight models, with only 14.24M parameters and 2.55G FLOPs. It demonstrates superior robustness in complex agricultural environments, balancing performance, efficiency, and deployability.