13 April 2018 Convolutional neural network with transfer learning for rice type classification
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 1069613 (2018) https://doi.org/10.1117/12.2309482
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
Abstract
Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.
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Vaibhav Amit Patel, Vaibhav Amit Patel, Manjunath V. Joshi, Manjunath V. Joshi, } "Convolutional neural network with transfer learning for rice type classification", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 1069613 (13 April 2018); doi: 10.1117/12.2309482; https://doi.org/10.1117/12.2309482
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