There is a large number of flowers available in the world, and it is hard to remember all names and types of flowers, but for identification and recognition of flower species in environments such as forests, mountains, and dense regions is necessary to know about their existence. So the system which is developed for identification of flower type is useful. This identification and recognition of a particular flower among millions of flower types is a very heavy task. So Automated flower species recognition has been studied for many years. Differences between these studies come from features that were extracted from the flower image and the recognition algorithm that was used to recognize the flower species. For selecting the feature from flower images, the three most important attributes to be considered are color, texture, and shape. For these individual class of feature variety of feature extraction methods are present, and for recognition, the different classification model is present such as ANN, kNN, SVM, CNN, etc. This paper discusses, and well us reviews the algorithms and the technologies which are available for segmentation, feature extraction, classifying, detecting and counting of flowers from the flower images from different standardized dataset like Oxford 17, Oxford 102, etc and analyzing several research papers.

Segmentation,feature extraction,classification ,SVM, shape, texture, color

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