基于"点-面-时空"多源数据融合的作物病虫害多目标决策方法OA

Aiming to address the limitations of insufficient multi-source data fusion,weak multi-model collaboration,and limited engineering applicability in intelligent crop disease and pest management,a multi-objective decision-making method based on plant electronic medical records(PEMRs)was proposed.Taking PEMRs as the core data carrier,structured,textual,and image data were integrated to construct a"point-surface-spatiotemporal"framework,enabling continuous decision support from individual diagnosis to regional analysis and spatiotemporal prediction.At the"point"level,a Shared-MMoE model was developed for structured data to jointly optimize diagnosis and prescription recommendation;a BERT-CNN model was employed for semantic diagnosis of textual data;and a lightweight image recognition model based on EfficientNet-B3 with ECA attention was constructed.In addition,a knowledge graph combined with large language models was introduced for knowledge representation and interpretable reasoning.At the"surface"level,temporal filtering and regional aggregation of PEMR data were conducted to visualize spatial disease distribution.At the"spatiotemporal"level,a KAST-Graph model was developed to predict disease trends across multiple regions.Based on these,a practical multi-objective decision-making platform for crop pest and disease management was developed as a WeChat mini-program.Experimental results showed that the structured diagnosis achieved an AUC of 96.33％,text-based diagnosis accuracy reached 93.13％,and in the validation set pest and disease recognition accuracy of 85.95％.The proposed model outperformed baseline methods in MAE,RMSE,and MAPE.The system achieved an average response time of less than 0.5 s,with a multi-modal diagnostic consistency of 94.0％.These results demonstrated that the proposed method operated stably under lightweight deployment conditions and effectively supported multi-objective decision-making in crop disease and pest management.