Abstract
To improve the accuracy of automatic recognition and classification of vegetables, this paper presents a method of recognition and classification of vegetable image based on deep learning, using the open source deep learning framework of Caffe, the improved VGG network model was used to train the vegetable image data set. We propose to combine the output feature of the first two fully connected layers (VGG-M). The Batch Normalization layers are added to the VGG-M network to improve the convergence speed and accuracy of the network (VGG-M-BN). The experimental verification, this paper method in the test data set on the classification of recognition accuracy rate as high as 96.5%, compared with VGG network (92.1%) and AlexNet network (86.3%), the accuracy rate has been greatly improved. At the same time, increasing the Batch Normalization layers make the network convergence speed nearly tripled. Improve the generalization ability of the model by expanding the scale of the training data set. Using VGG-M-BN network to train different number of vegetable image data sets, the experimental results show that the recognition accuracy decreases as the number of data sets decreases. By contrasting the activation functions, it is verified that the Rectified Linear Unit (ReLU) activation function is better than the traditional Sigmoid and Tanh functions in VGG-M-BN networks. The paper also verifies that the classification accuracy of VGG-M-BN network is improved due to the increase of batch_size.
Article PDF
Avoid common mistakes on your manuscript.
References
N.-Q. Pham, T.-S. Nguyen, J. Niehues, et al., Very deep self-attention networks for end-to-end speech recognition, arXiv preprint arXiv: 1904.13377, 2019.
L. Zhu, Z. Li, C. Li, et al., High-performance vegetable classification from images based on AlexNet deep learning model, Int. J. Agric. Biol. Eng. 11 (2018), 217–223.
S. Ciptohadijoyo, W.S. Litananda, M. Rivai, et al., Electronic nose based on partition column integrated with gas sensor for fruit identification and classification, Comput. Electr. Agric. 121 (2016), 429–435.
P. Ninawe, S. Pandey, A completion on fruit recognition system using K-nearest neighbors algorithm, Int. J. Adv. Res. Comput. Eng. Technol. 3 (2014), 2352–2356.
S.R. Dubey, S. Jalal, Fruit and vegetable recognition by fusing color and texture features of the image using matching learning, Int. J. Appl. Pattern Recognit. 2 (2015), 160–181.
Y. Zhang, S. Wang, G. Ji, P. Phillips, Fruit classification using computer vision and feedforward neural network, J. Food Eng. 143 (2014), 167–177.
H.W. Tao, L. Zhao, J. Xi, et al., Fruits and vegetables recognition based on color and texture features, Trans. Chin. Soc. Agric. Eng. 30 (2014), 305–311.
D.H. Hubel, T.N. Wiesel, Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex, Physiol. 160 (1962), 106–154.
S.H. Lee, C.S. Chan, S.J. Mayo, et al., How deep learning extracts and learns leaf features for plant classification, Pattern Recognit. 71 (2017), 1–13.
P. Wang, W. Li, S. Liu, et al., Large-scale isolated gesture recognition using convolutional neural networks, in International Conference on Pattern Recognition, IEEE, Cancun, Mexico, 2016, pp. 7–12.
X.W. Gao, R. Hui, Z. Tian, Classification of CT brain images based on deep learning networks, Comput. Methods Prog. Biomed. 138 (2017), 49–56.
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst. 25 (2012).
G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science. 7 (2006), 504–507.
G. Hinton, A practical guide to training restricted Boltzmann machines, Momentum. 9 (2010), 926–947.
G.E. Hinton, S. Osindero, Y.W. The, A fast learning algorithm for deep belief nets, Neural Comput. 18 (2006), 1527–1554.
C. Hentschel, T.P. Wiradarma, H. Sack, Fine tuning CNNS with scarce training data — adapting imagenet to art epoch classification, in IEEE International Conference on Image Processing, IEEE, Phoenix, AZ, USA, 2016, pp. 3693–3697.
V. Ferrari, M. Guillaumin, Large-scale knowledge transfer for object localization in ImageNet, in IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Providence, RI, USA, 2012, pp. 3202–3209.
Y. Lecun, Y. Bengio, G. Hinton, Deep learning, Nature. 521 (2015), 436–444.
X. Zeng, L.I. Jie, Time-frequency image recognition based on convolutional neural network, Machinery & Electronics, 34 (2016), 25–29.
T. Zhou, An image recognition model based on improved convolutional neural network, J. Comput. Theor. Nanosci. 13 (2016), 4223–4229.
M. Alotaibi, A. Mahmood, Improved Gait recognition based on specialized deep convolutional neural networks, in Applied Imagery Pattern Recognition Workshop, IEEE, Washington, DC, USA, 2015, pp. 1–7.
Z. Kaixuan, H. Dongjian, Recognition of individual dairy cattle based on convolutional neural networks, Trans. Chin. Soc. Agric. Eng. 31 (2015), 181–187.
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, 2014. https://arxiv.org/abs/1409.1556
A. Singla, L. Yuan, T. Ebrahimi, Food/non-food image classification and food categorization using pre-trained GoogLeNet model, in International Workshop on Multimedia Assisted Dietary Management, ACM, Amsterdam, The Netherlands, 2016, pp. 3–11.
S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv: 1502.03167, 2015. https://arxiv.org/abs/1502.03167
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This is an open access article distributed under the CC BY-NC 4.0 license (https://doi.org/creativecommons.org/licenses/by-nc/4.0/).
About this article
Cite this article
Li, Z., Li, F., Zhu, L. et al. Vegetable Recognition and Classification Based on Improved VGG Deep Learning Network Model. Int J Comput Intell Syst 13, 559–564 (2020). https://doi.org/10.2991/ijcis.d.200425.001
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.2991/ijcis.d.200425.001