| 基于改进Hu矩和遗传神经网络的稻飞虱识别系统 |
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| 引用本文： | 邹修国,丁为民,刘德营,赵三琴.基于改进Hu矩和遗传神经网络的稻飞虱识别系统[J].农业机械学报,2013,44(6):222-226. |
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| 作者姓名： | 邹修国 丁为民 刘德营 赵三琴 |
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| 作者单位： | 南京农业大学江苏省现代设施农业技术与装备工程实验室,南京,210031 |
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| 基金项目： | 国家高技术研究发展计划(863计划)资助项目(2012AA101904)、公益性行业（农业）科研专项资助项目（201203059）和南京农业大学青年科技基金资助项目（KJ2010031） |
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| 摘 要： | 针对稻飞虱识别实时性差和BP神经网络分类有一定误差的问题,设计了1种基于DSP硬件平台和遗传神经网络算法的稻飞虱识别系统.系统硬件以AT89S52单片机控制拍摄移动装置,以DM6437处理器作为算法处理平台；系统软件设计主要包括基于改进Hu矩的特征值提取和基于遗传算法优化神经网络的识别算法.系统通过CCD摄像机拍摄稻飞虱视频信号传送到DSP识别系统,从中提取图像,识别图像中的稻飞虱.实验对稻飞虱、水蝇和潜蝇等80个样本进行了训练和测试,结果表明遗传神经网络对稻飞虱的正确识别率达到90％.
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| 关 键 词： | 稻飞虱 图像识别 改进Hu矩 遗传算法 神经网络 |
Recognition System of Rice Planthopper Based on Improved Hu Moment and Genetic Algorithm Optimized BP Neural Network |
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| Zou Xiuguo,Ding Weimin,Liu Deying and Zhao Sanqin.Recognition System of Rice Planthopper Based on Improved Hu Moment and Genetic Algorithm Optimized BP Neural Network[J].Transactions of the Chinese Society of Agricultural Machinery,2013,44(6):222-226. |
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| Authors: | Zou Xiuguo Ding Weimin Liu Deying and Zhao Sanqin |
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| Institution: | Nanjing Agricultural University;Nanjing Agricultural University;Nanjing Agricultural University;Nanjing Agricultural University |
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| Abstract: | For the problems of poor real-time of rice planthopper recognition and a certain error of BP neural network classifier, a rice planthopper recognition system was designed based on DSP hardware system and genetic algorithm optimized BP neural network. AT89S52 microcontroller was used to control the mobile device. DM6437 was used as processing platform. Mathematical morphology algorithm, improved Hu moment, and genetic algorithm optimized BP neural network algorithm were used for segmentation. The video camera was used to shoot crop video. Then, the video signal images were transformed to the DSP recognition system. The rice planthopper could be identified from these images. The experiment was carried out on 80 samples, including rice planthopper, ephydrid and miner. Results showed that the accuracy of genetic algorithm optimized BP neural network reached to 90%. |
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| Keywords: | Rice planthopper Image recognition Improved Hu moment Genetic algorithm BP neural network |
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