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However, traditional leaf recognition methods can be easily affected by environments and cannot realize multi-leaf recognition under a complex background in real time. In this work, we present a real-time leaf recognition method based on image segmentation and feature recognition. First, we denoise the input of a leaf image, performing a leaf segmentation with an improved FCN network model, and then optimize the contour edge with a CRF algorithm to get a leaf segmentation image. Second, we extract the content features of the segmented leaf image with an Inception-V2 network model to get a feature map of the leaf image. Third, we input the feature map into an RPN network to obtain a set of regional candidate frames and then integrate the feature map and the information of candidate frames in a RoI Pooling layer, which can extract the feature map of a candidate frame area and scale it to a fixed-size feature map. Finally, we send the feature map to a fully connected layer to classify each preselection box content through the calculation of preselection feature maps, and then obtain the final accurate position of the prediction box by utilizing a bounding box regression. The experimental results show that the proposed method can achieve multi-leaf recognitions with high accuracy and fast speed under complex environments in real time. <\/jats:p>","DOI":"10.1142\/s0218001421540331","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T02:17:41Z","timestamp":1643595461000},"source":"Crossref","is-referenced-by-count":3,"title":["Real-Time Leaf Recognition Method Based on Image Segmentation and Feature Extraction"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4885-3312","authenticated-orcid":false,"given":"Xingquan","family":"Cai","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]},{"given":"Yuqing","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]},{"given":"Yunbo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]},{"given":"Mengyao","family":"Xi","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]},{"given":"Yuxin","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]},{"given":"Chen","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]},{"given":"Haiyan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"issue":"12","key":"S0218001421540331BIB001","first-page":"2481","volume":"39","author":"Badrinarayanan V.","year":"2017","journal-title":"Computer Science"},{"issue":"5","key":"S0218001421540331BIB002","first-page":"671","volume":"22","author":"Cao S. 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