Abstract: 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 ImageSharp and color recognition technology CV2 are used to realize intelligent retrieval of plant images. FastDFS 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.

Key words: plant image, retrieval system, big data, artificial intelligence, distributed storage, search engine, Web crawler