Knowledge graph embedding (KGE) has been identified as an effective method for link prediction, which involves predicting missing relations or entities based on existing entities or relations. KGE is an important method for implementing knowledge representation and, as such, has been widely used in driving intelligent applications w.r.t. question-answering systems, recommendation systems, and relationship extraction. Models based on convolutional neural networks (CNNs) have achieved good results in link prediction. However, as the coverage areas of knowledge graphs expand, the increasing volume of information significantly limits the performance of these models. This article introduces a triple-attention-based multi-channel CNN model, named ConvAMC, for the KGE task. In the embedding representation module, entities and relations are embedded into a complex space and the embeddings are performed in an alternating pattern. This approach helps in capturing richer semantic information and enhances the expressive power of the model. In the encoding module, a multi-channel approach is employed to extract more comprehensive interaction features. A triple attention mechanism and max pooling layers are used to ensure that interactions between spatial dimensions and output tensors are captured during the subsequent tensor concatenation and reshaping process, which allows preserving local and detailed information. Finally, feature vectors are transformed into prediction targets for embedding through the Hadamard product of feature mapping and reshaping matrices. Extensive experiments were conducted to evaluate the performance of ConvAMC on three benchmark datasets compared with state-of-the-art (SOTA) models, demonstrating that the proposed model outperforms all compared models across all evaluation metrics on two of the datasets, and achieves advanced link prediction results on most evaluation metrics on the third dataset.

In agricultural disease images, lesions often exhibit large-scale variations, irregular shapes, gradual boundary transitions, and high texture similarity to surrounding backgrounds. These characteristics make models prone to insufficient lesion-body response, biased contextual discrimination, and unstable contour localization during segmentation. To address the fine-grained segmentation difficulties caused by imbalanced regional representation, semantic discrimination, and boundary constraints, this paper proposes DHB-DeepLabV3+, an agricultural disease image segmentation model. Based on lightweight feature extraction, the proposed model further organizes lesion segmentation beyond pixel-wise classification by jointly modeling lesion regions, contextual semantics, and boundary structures, thereby improving the completeness and boundary stability of disease regions in complex scenes. The proposed model was evaluated on three datasets, including a rice disease dataset, a leaf disease dataset, and the PlantSeg dataset. Across five independent runs, DHB-DeepLabV3+ achieved mean mIoU values of 88.45%, 82.52%, and 81.72% on the rice disease, leaf disease, and PlantSeg datasets, respectively, with corresponding standard deviations of 0.26, 0.35, and 0.38 percentage points. The lightweight version reduced the number of parameters to 6.67 M and the computational cost to 2.36 GFLOPs, and it was deployed on an RK3568 edge device for validation. Experimental results show that the proposed method achieves stable segmentation performance and has potential for edge-device application in complex agricultural disease segmentation scenarios.