Development of Feature Based Classification of Fruit using Deep Learning
Yogesh1, Ashwani Kumar Dubey2, Rajeev Ratan3

1Yogesh*, Electronics & Communication, Amity University Uttar Pradesh, Noida, India.
2Ashwani Kumar Dubey, Electronics & Communication, Amity University Uttar Pradesh, Noida, India.
3Rajeev Ratan, Electronics & Communication, MVN University, Palwal, India. 

Manuscript received on September 14, 2019. | Revised Manuscript received on 23 September, 2019. | Manuscript published on October 10, 2019. | PP: 3585-3290 | Volume-8 Issue-12, October 2019. | Retrieval Number: L28041081219/2019©BEIESP | DOI: 10.35940/ijitee.L2804.1081219
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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.
Keywords: Classification, Deep learning, Convolutional Neural Network, GoogleNet, AlexNet
Scope of the Article: Classification