Abstract:

The identification of various types of flowers and leaves based on their characteristics plays a crucial role in agriculture and medical research. This paper explores the application of machine learning algorithms for flower identification, focusing on key features such as petal length, petal width, sepal length, and sepal width. The algorithms used include K-Nearest Neighbor (KNN), Random Forest, and Decision Tree, applied to a flower dataset. The performance of each algorithm is evaluated based on its precision. The implementation is carried out using Python programming. The results demonstrate that the KNN algorithm outperforms the other algorithms in terms of accuracy for flower detection.

Description:

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