CNN based approach for identifying banana species from fruits

Classification and recognition of fruits are still the challenging one as there are different classes of fruit types having wide inter-class resemblance. This paper proposes a banana identification model using a five-layer convolution neural network (CNN) which composed of convolution layer, pooling layer and fully connected layer. Fruits like Apple, Strawberry, and Orange, Mango and banana have been analyzed and several features have been extracted using deep learning-based CNN algorithm. Finally, the fruit identification process is done by random forest and K-Nearest Neighborhood (K-NN) classifying algorithms. A regular digital camera is used for the image acquisition process. All image manipulation processes are performed in MATLAB-17 environment.Experiments are conducted in our database consisting of 5887 fruit images. The performance of the proposed deep learning based-random forest and KNN classifiers are compared with the existing feature extraction method of HOG based random forest and KNN in terms of accuracy, precision, recall and f-score and we have achieved a accuracy rate of 96.98% for deep feature-random forest classification combination algorithm which is better than the deep feature-KNN, HOG based KNN and random forest classifiers. © 2020, Bharati Vidyapeeth's Institute of Computer Applications and Management.