面向海量植物图像的智能检索系统设计

Design of Intelligent Retrieval System for Massive Plant Images

针对传统软件技术设计的植物图像检索系统中存在无法实现智能检索、植物图像数量增长慢、检索系统难以扩容,以及当植物图像数量达到百万级以上时检索效率低和检索请求高并发时植物图像加载慢等问题,提出利用百度AI技术、Image Sharp图像分割技术和CV2颜色识别技术实现植物图像的智能检索。利用Fast DFS技术实现检索系统的动态扩容、负载均衡和植物图像的快速加载,利用Solr搜索引擎技术提高海量植物图像的检索效率,利用Python爬虫技术不断丰富检索系统的植物图像从而实现检索系统的可持续化发展。实验结果表明,通过上述技术能够构建一个面向海量植物图像的智能检索系统。

In view of the problems of the plant image retrieval system designed by traditional software technology,such as unable to realize intelligent retrieval,slow growth of the number of plant images,difficult expansion of the retrieval system,low retrieval efficiency when the number of plant images reaches more than one million,and slow loading of plant images when the retrieval requests are highly concurrent,Baidu AI technology,image segmentation technology Image Sharp and color recognition technology CV2 are used to realize intelligent retrieval of plant images. Fast DFS technology is used to realize the dynamic expansion,load balancing and rapid loading of plant images of the retrieval system,Solr search engine technology is used to improve the retrieval efficiency of massive plant images,and Python crawler technology is used to continuously enrich the plant images of the retrieval system,so as to realize the sustainable development of the retrieval system. The experimental results show that the above technology can build an intelligent retrieval system for massive plant images.