A recognition model of red jujube disease severity based on improved PSO-BP neural network

摘要：

In order to improve the accuracy of the red jujube disease recognition, the study establishes a recognition model of disease severity, with the improved Particle Swarm Optimization Back Propagation (PSO-BP) neural network combined with color and geometry characteristic parameters of red jujube tree leaf disease spot. Mutation operator and linear decrease inertia weight are combined to improve the performance of PSO, a new improved PSO is formed to get optimal neural network weights and thresholds. The experimental results show that the accuracy and performance of red jujube disease recognition model is improved. The slight, general and serious disease reached separately 87.6％, 82.4％ and 94.0％.

关键词： mutation operator linear decrease inertia weight Particle Swarm Optimization Back Propagation neural network red jujube disease recognition model

作者: BAI Tie-cheng JIANG Qing-song XING Wei MENG Hong-bing

作者单位: College of Information Engineering Tarim university Xinjiang, China College of Computer Science and Technology Zhejiang university Zhejiang, Country

会议类型: 国际会议

会议名称: 2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

会议地点: 杭州

会议语种:英文

页码: 670-673

在线出版日期: 2012-03-23（万方平台首次上网日期，不代表论文的发表时间）