The development of smart agriculture has created an urgent demand for efficient and accurate weed recognition and detection technologies. However, the diverse and complex morphology of weeds, coupled with the scarcity of labeled data in agricultural scenarios, poses significant challenges to traditional supervised learning methods. To address these issues, a weed detection model based on a semi-supervised diffusion generative network is proposed. This model integrates a generative attention mechanism and semi-diffusion loss to enable the efficient utilization of both labeled and unlabeled data. Experimental results demonstrate that the proposed method outperforms existing approaches across multiple evaluation metrics, achieving a precision of 0.94, recall of 0.90, accuracy of 0.92, and mAP@50 and mAP@75 of 0.92 and 0.91, respectively. Compared to traditional methods such as DETR, precision and recall are improved by approximately 10% and 8%, respectively. Additionally, compared to the enhanced YOLOv10, mAP@50 and mAP@75 are increased by 1% and 2%, respectively. The proposed semi-supervised diffusion weed detection model provides an efficient and reliable solution for weed recognition and introduces new research perspectives for the application of semi-supervised learning in smart agriculture. This framework establishes both theoretical and practical foundations for addressing complex target detection challenges in the agricultural domain.

In the era of big-data-driven multi-platform and multimodal health information dissemination, the rapid spread of false and misleading content poses a critical threat to public health awareness and decision making. To address this issue, a dual-stream Transformer-based multimodal health misinformation detection framework is presented, incorporating a symbol drift detection module, a symbol-aware text graph neural network, and a crossmodal alignment fusion module. The framework enables precise identification of implicit misleading health-related symbols, comprehensive modeling of textual dependency structures, and robust detection of crossmodal semantic conflicts. A domain-specific health-symbol-sensitive lexicon is constructed, and contextual drift intensity is quantitatively measured and embedded as explicit features into the text GNN. Bidirectional cross-attention and contrastive learning are further employed to enhance crossmodal semantic alignment. Extensive experiments on a large-scale real-world multimodal health information dataset, encompassing heterogeneous data sources typical of big data environments, demonstrate that the proposed method consistently outperforms state-of-the-art baselines in CTR prediction, multimodal recommendation, and ranking tasks. The results indicate substantial improvements in both accuracy and ranking quality, while ablation studies further verify the contributions of symbol drift modeling, graph-structured representation, and crossmodal fusion. Overall, the proposed approach advances big data analytics for multimodal misinformation detection and provides an interpretable and scalable solution for public health communication governance.

Under the intensifying impacts of climate change, tightening agricultural resource constraints, and escalating food security pressures, the development of high-accuracy and interpretable crop yield estimation methods has become a critical technical issue in sustainable agricultural engineering. In this study, multi-temporal and multi-spectral remote sensing imagery are utilized as the core input. A multi-scale visual feature extraction module is designed to characterize canopy texture, field structure, and regional heterogeneity, while a temporal growth modeling module captures the dynamic evolution of crops from emergence to maturity. Yield regression is further integrated with economic mapping and explainability mechanisms, thereby forming an end-to-end prediction framework. Experimental results across multiple regions and years demonstrate that the proposed method outperforms various representative models. In the primary regression experiment, the framework achieves approximately $R^2=0.76$, with MAE reduced to 0.60 and MSE to 0.62, representing an error reduction of over 25% compared with traditional regression approaches and classical machine learning models. In classification experiments for yield-grade evaluation, the model attains an accuracy of approximately 0.85, with both precision and recall exceeding 0.82, demonstrating its effectiveness in both continuous yield prediction and stable yield-level region identification. Cross-region and cross-year validation further indicate strong generalization capability, with $R^2$ remaining above 0.65 in unseen regions and around 0.67 under cross-year prediction settings. Ablation studies confirm the synergistic contributions of multi-scale spatial modeling, temporal growth modeling, and explainability constraints, as performance consistently declines when any individual module is removed. Overall, the results highlight that the proposed framework provides reliable data support for precision agricultural management, resource optimization, and agricultural engineering decision-making, while also offering a scalable and reproducible pathway for sustainable agricultural engineering development.

To address prevalent challenges in field-based wheat pest recognition—namely, viewpoint perturbations, sample scarcity, and heterogeneous data distributions—a pest identification framework named CropCLR-Wheat is proposed, which integrates self-supervised contrastive learning with an attention-enhanced mechanism. By incorporating a viewpoint-invariant feature encoder and a diffusion-based feature filtering module, the model significantly enhances pest damage localization and feature consistency, enabling high-accuracy recognition under limited-sample conditions. In 5-shot classification tasks, CropCLR-Wheat achieves a precision of 89.4%, a recall of 87.1%, and an accuracy of 88.2%; these metrics further improve to 92.3%, 90.5%, and 91.2%, respectively, under the 10-shot setting. In the semantic segmentation of wheat pest damage regions, the model attains a mean intersection over union (mIoU) of 82.7%, with precision and recall reaching 85.2% and 82.4%, respectively, markedly outperforming advanced models such as SegFormer and Mask R-CNN. In robustness evaluation under viewpoint disturbances, a prediction consistency rate of 88.7%, a confidence variation of only 7.8%, and a prediction consistency score (PCS) of 0.914 are recorded, indicating strong stability and adaptability. Deployment results further demonstrate the framework’s practical viability: on the Jetson Nano device, an inference latency of 84 ms, a frame rate of 11.9 FPS, and an accuracy of 88.2% are achieved. These results confirm the efficiency of the proposed approach in edge computing environments. By balancing generalization performance with deployability, the proposed method provides robust support for intelligent agricultural terminal systems and holds substantial potential for wide-scale application.