Efficient deep features selections and classification for flower species recognition

•This study applies convolutional neural network to flower species classification.•It employs a pre-trained DCNN model for feature extraction.•Minimum Redundancy Maximum Relevance (mRMR) method is used to select features.•A support vector machine (SVM) classifier is employed to classify the flower species.•It achieved 96.39% and 95.70% accuracy performance for Flower17 and Flower102.