Identification of plants species has become one of the challenges for image processing and machine learning. The need to find an efficient solution to such a problem is essential as medicinal plants and new plants’ existence need to be studied. Most of the researches in identifying this plants species are based on color, shape and textures. This paper is based on these features with Data-Augmentation. Data-augmentation is an important technique in increasing the number of training dataset which further helps in increasing the prediction of classification. This paper uses machine learning algorithms in classifying the flower classes based on FLOWERS17 dataset. Data-augmentation is applied to the training dataset to enhance the prediction. It has been observed that Random Forest classifies flowers with an accuracy of 64% before data-augmentation and 94% after data-augmentation. This paper also shows that after increasing the number of classes from 17 to 21, the performance of Random Forest is consistent to 94%.

Data Augmentation, Flower Recoginition, Image Processing, Machine Learning

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