<![CDATA[Flowers are one of the organs of the plant body that function for generative propagation which has various forms and ways of working according to the type, but for plants that have seeds, these tools are usually important for plants that we know as flowers. Flowers are an important item in the object recognition process. The item recognition process in the computerized division is very important for determining the foundation and forefront of an image. It expects to get the spotlight it needs. The flower image in this study has a complicated picture which is very inconvenient because there are leaves and trees around the flower image. So, in this case concentrate on the proposed division involving Otsu Threshold as a strategy to isolate views and closer foundations. The division process is very firm to get shape highlights such as area, eccentricity, and perimeter. utilizing the computation of these elements will be sorted using the calculation of the Naïve Bayes algorithm by utilizing 120 flower images from 17 flower datasets. The dataset will be partitioned into test information and prepare information, and take advantage of cross-consensus (k=10). ensure that the settings using Naïve Bayes get a higher precision level of 99.168% with a relative absolute error of 9.0284%]]>
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