Intra Class Flower Recognition System Using Machine Learning and Neural Networks

This comparative study of three machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbours (KNN), and Recurrent Neural Networks (RNN), for Intra class flower classification. The study's objective is to identify the most effective approach for classifying different types of flowers within the same class. To evaluate the performance of these algorithms, the study uses a collected dataset of Intra class flower images and measures their accuracy, precision, and recall. The evaluation is based on the confusion matrix, which shows the number of correct and incorrect predictions for each class. SVM achieved the highest accuracy rate of 95.43%, followed by RNN with an accuracy rate of 97.009%, and KNN with an accuracy rate of 93.96%. While KNN has faster training and prediction time, RNN is better suited for sequential data, such as temporal aspects of flower growth. SVM, on the other hand, excels in linearly separable data. The study provides insights into the strengths and limitations of each algorithm for Intraclass flower classification. The results of this study have significant implications for the development of automated flower recognition systems. By identifying the most effective approach for classifying different types of flowers within the same class, this research can help improve the accuracy of such systems, which are increasingly being used in various fields, including agriculture, ecology, and conservation. In conclusion, the comparative study of Deep learning technique i.e, RNN and Machine learning techniques i.e, SVM and KNN demonstrates the strengths and limitations of each algorithm, providing valuable insights for researchers and practitioners in the field of machine learning. This study's findings can be used to improve the development of more effective automated flower recognition systems, with potential applications in diverse fields.