Fruit Image Classification Using Deep Learning

Fruit classification is found to be one of the rising fields in computer and machine vision. Many deep learning-based procedures worked out so far classify images may have some ill-posed issues. The performance scheme depends on range captured images, volume features, types characters, choice features from extracted type classifiers used. This paper aims propose a novel learning approach consisting Convolution Neural Network (CNN), Recurrent (RNN), Long Short-Term Memory (LSTM) application fruit images. Classification accuracy selected optimal features. Deep applications CNN, RNN, LSTM were collectively involved fruits. CNN used extract image RNN select fruits based by RNN. Empirical study shows supremacy proposed over existing Support Vector Machine (SVM), Feed-forward (FFNN), Adaptive Neuro-Fuzzy Inference System (ANFIS) competitive techniques for classification. rate quite better than SVM, FFNN, ANFIS schemes. It has been concluded that technique outperforms