Development of feature based classification of fruit using deep learning

Abstract

Fruit classification is a challenging task in image processing. Computer vision based classification method is agile and rigorous compared to human based approach. In this paper, a method is developed for feature classification using deep learning. The region with their own characteristics is classified based on deep learning convolutional neural network technique. Traditional method for diagnosis of fruit involves visual observations by experts. The interference of environmental factors needs to be considered during diagnosis process. Datasets such as VOC, PASCAL, ImageNet etc. are easily available that are used for training of several different types of objects. The proposed model introduces two pre-trained networks; AlexNet and GoogLeNet. For faster and optimized training, Rectified linear unit (ReLu) is used that maintain positive value and map negative values to zero. The model learns to perform classification directly from images. Neural network architecture is used for implementation of deep learning. Error in deep learning is minimized compared to machine learning. The high end GPU’s reduces the training time. A transfer learning technique is proposed to retrain the network that is capable of performing new recognition task.