Crop diseases not only severely affect crop quality but also lead to significant reductions in yield. To address the challenge of accurate crop disease detection in complex environments, we propose a novel detection method based on the YOLO11 model, termed YOLO-MSCM. To enhance the extraction of small-object features, we introduce the MCSA module, which improves the model’s spatial feature perception. Additionally, a SimRepHMS module is designed to leverage local contextual information and strengthen multi-scale feature fusion. To improve the model’s adaptability and generalization capability, we employ DynamicConv, which dynamically adjusts channel weights based on input-specific patterns. For more accurate bounding box localization, we incorporate the WIoUv3 loss function to optimize box regression. Experimental results demonstrate that YOLO-MSCM achieves improvements of 6.1% in precision, 9.1% in recall, 6.2% in mAP@50, and 3.7% in mAP@50:95 compared to the baseline YOLO11n model. Comparative evaluations with several mainstream and state-of-the-art models further validate the superior detection performance of YOLO-MSCM, offering a reliable and effective solution for accurate crop disease detection in complex scenarios.

For the single image dehazing problem, an end-to-end multi-stage dehazing algorithm is designed. The algorithm contains two distinct parts to extract features. For shallow features, texture-level information is mined by stacking pixel and channel attention mechanisms. The proposed method uses multi-head self-attention (MHSA) to capture high-level features. MHSA improves dehazing performance by mining the dependencies of a wide range of abstract information. The superiority of the transformer architecture is extended with cascaded attention mechanisms and convolutions to improve feature extraction capabilities. Multilayer perceptron (MLP) is used in the decoding stage to equalize the context information. Furthermore, a contrastive loss function that introduces multiple negative samples and correction terms is proposed. The correction term is generated according to the difference between the precise and blurred images, which can enhance the training effect when dealing with different concentrations of dehaze. The training result of this loss function assists the model in approximating clear images and staying away from blurry images. According to the experimental results compared with other methods under the same conditions, the proposed method achieves good results in both subjective visual effects and objective evaluation indicators. The proposed contrast loss function also improves the dehazing performance of the algorithm.

Cross-domain UAV operations require secure and privacy-preserving authentication across multiple administrative domains. However, existing schemes still suffer from centralized authorization, exposure of mission attributes, insufficient revocation consistency, and limited auditability. To address these issues, this paper proposes a blockchain-assisted revocable capability credential authentication protocol for cross-domain UAV edge networks. The main innovation is to shift cross-domain UAV authentication from identity- or location-centric verification to revocation-aware lifecycle verification of mission capabilities. In the proposed framework, a Domain Authority committee jointly issues blinded capability credentials through distributed key generation and threshold BLS signatures. UAVs then prove mission-policy compliance using Groth16 zero-knowledge proofs without revealing sensitive capability attributes. Meanwhile, the blockchain maintains credential active states, revocation versions, and audit-root evidence for cross-domain consistency and accountability. A ticket-based AuthResume mechanism is further designed to reduce repeated authentication overhead while preventing outdated tickets from bypassing revocation. Formal security analysis, BAN logic, and Scyther verification show that the protocol supports credential unforgeability, revocation enforcement, auditability, mutual authentication, freshness, and session synchronization. Solidity smart contracts were deployed on the Sepolia testnet, and experimental results demonstrate acceptable FullAuth overhead and lightweight repeated access through AuthResume.