基于IPSO-BP模型的牧草产量预测方法研究OA

To address the multi-factor coupling challenges in forage yield prediction,this study focuses on the Shuozhou municipal area as the research subject.By integrating data(including temperature,precipitation,sunshine duration,and 10 cm ground temperature data)from six national basic meteorological stations from 2005 to 2022,and coupling with the region's annual forage yield data and growth-stage climate re-quirement characteristics over the past decade,established a BP neural network-based foundational prediction model.The model parameters were optimized through the introduction of standard particle swarm optimization(PSO)and improved particle swarm optimization(IPSO)algo-rithms.Simulation comparative studies demonstrate that the IPSO-optimized BP neural network achieves significant performance enhancement in prediction accuracy,exhibiting lower Mean Absolute Error compared with both the basic BP model and PSO-BP model.The algorithm-im-proved model also demonstrates stronger generalization capabilities,with IPSO showing higher global optimization efficiency than basic PSO.These findings validate the application value of the improved algorithm for forage yield prediction under complex meteorological conditions.