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Support Vector Machine-based Flower Image Classification for Commercial Applications

Shubham Sarvade, Sneha Mhatre, Aditi Sawant, Babeetta Bbhagat

Abstract


Flowers are crucial raw materials in many industries, including pharmaceuticals and cosmetics. Classifying flowers manually is time-consuming, inconsistent, and requires professional judgment from a botanist. Computer-aided flower classification is a promising approach to this problem. Image classification often employs support vector machines (SVMs) as widely favored algorithms. This study evaluated the performance of SVMs for flower image classification. The results showed that SVMs can achieve good accuracy on this task, with an accuracy of 85.2%. This suggests that SVMs could be used to develop computer-aided flower classification systems. Such systems could be used to automate the flower classification process in a variety of industries such as pharmaceutical and cosmetics companies can use SVMs to classify flowers based on their chemical composition, color, texture, and shape. The data can subsequently serve as a foundation for the creation of novel pharmaceuticals, therapies, and beauty products.


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