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Fine-grained classification of natural scene grape images based on deep convolution networks

Grape is an important fruit in the world, there are so many kinds of grapes that we must identify their category for the requirements of agricultural product quality inspection, it is difficult to realize the large-scale grape classification by traditional artificial methods. In the past several years, the accuracy of image classification had been improved due to the application of deep convolution networks, however, the recognition of natural scene fine-grained image is a difficult task in the field of computer vision. Usually, agricultural grapes grow in natural and complex orchard environment, the quality of photographed images will be influenced by illumination, shadow and blur, etc. It is hard to categorize these natural scene subclasses and interclass images due to the similarity and environmental interference of them. We obtain natural scene grape images and divide them into 9-category dataset for the first, and then divide the dataset into 10-category due to the significant difference in recall value of Yongyou-one grape, transfer learning based on Inception-v3 and a number of deep convolutional networks are used to analyze the classification performances of fine-grained image dataset, the size of network model and the number of dataset are analyzed respectively in the work. We obtain 98.494% classification accuracy on 10-category dataset, which is relative improvement to 0.8% on 9-category dataset, and the loss value of 10-category dataset is more stable than that of 9-category.