Indian Vegetable Image Classification Using Convolutional Neural Network
Abstract
– This paper represents a method of recognition and classification of vegetable images based on deep learning. Using the open source deep learning framework , the improved CNN network model was used to train the vegetable image data set. The classification accuracy of the CNN network is improved due to the hyperparameter tuning and layer addition. This study introduce a new, high-quality, dataset of images containing vegetables. This study also present the results of some numerical experiments for training a neural network to detect vegetables. The methods surveyed in this paper can distinguish among different kinds of vegetables in terms of their color and texture. In the future same experiment is going to be done and we will predict different types of vegetable’s disease. Along with local feature detection techniques, computer vision and pattern recognition are developing quickly.In this study, we extracted and learned the object to train the Deep Neural Network (DNN) for object category recognition. In order to recognize vegetable objects, we used deep learning and investigated the convolutional neural network (CNN). According to the evaluation findings, 3 million iterations were sufficient for the CNN vegetable identification learning process..The recognition rate was 97.50 %.
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References
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