人工智能大模型在林草有害生物防控领域的应用OA北大核心CHSSCDCSCD

Prevention and control of forest/grassland pests and diseases is crucial for ensuring ecological security.Currently,the monitoring and early-warning system in China is challenged by such issues as inadequate data integration and analysis,insufficient traditional monitoring methods,and insufficient application of artificial intelligence(AI)technologies,resulting in a relatively passive response.Large AI models based on the Transformer architecture,with core advantages including self-attention mechanisms and cross-domain generalization,provides an effective path for the intelligent transformation of the forest and grassland sectors.Focusing on the application of large AI models,this paper conducts a review from four perspectives:prediction and forecasting of forest/grassland pests and diseases,intelligent identification,knowledge query,and control with intelligent monitoring equipment.In terms of prediction and forecasting,large models break through the limitations of traditional methods to accurately predict the occurrence probability of pests and diseases by globally modeling multi-source ecological data.For intelligent identification,large models integrate the advantages of global and local feature extraction to process image data from multiple channels and thus improve recognition accuracy.As for knowledge query,multimodal large language models realize intelligent"image-to-image search"interaction,lower the professional threshold,and provide efficient prevention and control plans for staff.In terms of control with intelligent monitoring equipment,large models can be deployed on IoT devices to achieve rapid and intelligent monitoring of pests and diseases.Finally,technologies such as multimodal data fusion,knowledge graphs,and blockchain are used to discuss the prospect that large AI models use knowledge graphs to avoid"model hallucinations",expand cross-scenario applications,and construct a full-chain intelligent control system covering"prediction-identification-query-disposal"through model optimization and data integration,thereby improving the timeliness and accuracy of monitoring,early-warning and response.