Letian Yan

To comprehensively capture conversational emotion information within and between modalities, address the challenge of global and local feature modelling in conversation, and enhance the accuracy of multimodal conversation emotion recognition, we present a model called Multimodal Transformer and GNN for Emotion Recognition in Conversations (MTG-ERC). The model incorporates a multi-level Transformer fusion module that employs multi-head self-attention and cross-modal attention mechanisms to effectively capture interaction patterns within and between modalities. To address the shortcomings of attention-mechanism-based models in capturing short-term dependencies, we introduce a directed multi-relational graph fusion module, which employs directed graphs and multiple relation types to achieve efficient multimodal information fusion and to model short-term, speaker-dependent emotional shifts. By integrating the outputs of these two modules, the MTG-ERC model effectively combines global and local conversational emotion features and enhances intra-modal and inter-modal emotional interactions. The proposed model shows consistent improvements (around 1% absolute) in both accuracy and weighted F1 on the IEMOCAP and MELD datasets when compared with other baseline models. This highlights the model’s strong performance indicators and validates its effectiveness in comparison to existing models.

Agricultural image processing technology plays a critical role in enabling precise disease detection, accurate yield prediction, and various smart agriculture applications. However, its practical implementation faces key challenges, including environmental interference, data scarcity and imbalance datasets, and the difficulty of deploying models on resource-constrained edge devices. This paper presents a systematic review of recent advances in addressing these challenges, with a focus on three core aspects: environmental robustness, data efficiency, and model deployment. The study identifies that attention mechanisms, Transformers, multi-scale feature fusion, and domain adaptation can enhance model robustness under complex conditions. Self-supervised learning, transfer learning, GAN-based data augmentation, SMOTE improvements, and Focal loss optimization effectively alleviate data limitations. Furthermore, model compression techniques such as pruning, quantization, and knowledge distillation facilitate efficient deployment. Future research should emphasize multi-modal fusion, causal reasoning, edge–cloud collaboration, and dedicated hardware acceleration. Integrating agricultural expertise with AI is essential for promoting large-scale adoption, as well as achieving intelligent, sustainable agricultural systems.