<![CDATA[This research discusses object recognition in images, with a focus on flower recognition. Automatic flower identification has many practical applications, such as in agriculture, nature conservation, and botanical gardens. However, flower recognition remains a complex challenge because flowers come in a variety of shapes and colors. To overcome this problem, this research proposes the use of the Support Vector Machine (SVM) method in recognizing flower images based on color and shape features. SVM is a powerful classification algorithm and can perform well in cases where the features are in a high dimensional space. This research involves several stages, including image data collection, color and shape feature extraction, as well as training and testing of the SVM model. The SVM method was chosen because of its ability to handle high dimensional data and efficient memory usage. The experimental results show an SVM accuracy rate of 74.29%, indicating significant potential in flower image recognition using SVM with color and shape features.]]>
Copyrights © 2022
