This study addresses the challenges of elaeagnus angustifolia disease detection in smart agriculture by developing a detection system that integrates advanced deep learning technologies, including Large Language Models (LLMs), Agricultural Knowledge Graphs (KGs), Graph Neural Networks (GNNs), representation learning, and neural-symbolic reasoning techniques. The system significantly enhances the accuracy and efficiency of disease detection through an innovative graph attention mechanism and optimized loss functions. Experimental results demonstrate that this system significantly outperforms traditional methods across key metrics such as precision, recall, and accuracy, with the graph attention mechanism excelling in all aspects, particularly achieving a precision of 0.94, a recall of 0.92, and an accuracy of 0.93. Furthermore, comparative experiments with various loss functions further validate the effectiveness of the graph attention loss mechanism in enhancing model performance. This research not only advances the application of deep learning in agricultural disease detection theoretically but also provides robust technological tools for disease management and decision support in actual agricultural production, showcasing broad application prospects and profound practical value.

In this study, a deep-learning-based intelligent detection model was designed and implemented to rapidly detect cotton pests and diseases. The model integrates cutting-edge Transformer technology and knowledge graphs, effectively enhancing pest and disease feature recognition precision. With the application of edge computing technology, efficient data processing and inference analysis on mobile platforms are facilitated. Experimental results indicate that the proposed method achieved an accuracy rate of 0.94, a mean average precision (mAP) of 0.95, and frames per second (FPS) of 49.7. Compared with existing advanced models such as YOLOv8 and RetinaNet, improvements in accuracy range from 3% to 13% and in mAP from 4% to 14%, and a significant increase in processing speed was noted, ensuring rapid response capability in practical applications. Future research directions are committed to expanding the diversity and scale of datasets, optimizing the efficiency of computing resource utilization and enhancing the inference speed of the model across various devices. Furthermore, integrating environmental sensor data, such as temperature and humidity, is being considered to construct a more comprehensive and precise intelligent pest and disease detection system.

In this study, a deep learning method combining knowledge graph and diffusion Transformer has been proposed for cucumber disease detection. By incorporating the diffusion attention mechanism and diffusion loss function, the research aims to enhance the model’s ability to recognize complex agricultural disease features and to address the issue of sample imbalance efficiently. Experimental results demonstrate that the proposed method outperforms existing deep learning models in cucumber disease detection tasks. Specifically, the method achieved a precision of 93%, a recall of 89%, an accuracy of 92%, and a mean average precision (mAP) of 91%, with a frame rate of 57 frames per second (FPS). Additionally, the study successfully implemented model lightweighting, enabling effective operation on mobile devices, which supports rapid on-site diagnosis of cucumber diseases. The research not only optimizes the performance of cucumber disease detection, but also opens new possibilities for the application of deep learning in the field of agricultural disease detection.