Weed identification based on K-means feature learning combined with convolutional neural network

Computers and Electronics in Agriculture

Volumn 135, Issue , 2017, Pages 63-70

  Tang, JingLei ^a,c   Wang, Dong ^a   Zhang, ZhiGuang ^b   He, LiJun ^a   Xin, Jing ^c   Xu, Yang ^a  

^a NORTHWEST A F UNIVERSITY   (China)

^b HEBEI NORMAL UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c XI'AN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Convolutional neural network; K means clustering; Pre processing; Pre training; Weed identification model

Indexed keywords

CONVOLUTION; FEATURE EXTRACTION; NETWORK LAYERS; NEURAL NETWORKS;

CONVOLUTIONAL NEURAL NETWORK; K-MEANS CLUSTERING; PRE-PROCESSING; PRE-TRAINING; WEED IDENTIFICATION;

PARAMETER ESTIMATION;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; DESIGN; IDENTIFICATION METHOD; LEARNING; OPTIMIZATION; SOYBEAN; WEED;

GLYCINE MAX;

EID: 85011845619     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2017.01.001     Document Type: Article

References (19)

1. Abdi, H., Williams, L.J., Principal component analysis. Wiley Interdisciplinary Rev.: Comput. Stat. 2:4 (2010), 433–459.
2. Chen, D., Cao, X., Wen, F., Sun, J., Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. IEEE Conference on Computer Vision and Pattern Recognition, Washington, 2013, 3025–3032.
3. Coates, A., Ng, A.Y., Selecting receptive fields in deep networks. Adv. Neural Inform. Process. Syst., 2011, 2528–2536.
4. Dan, C.C., Meier, U., Gambardella, L.M., Schmidhuber, J., Convolutional neural network committees for handwritten character classification. Proceedings of the International Conference on Document Analysis and Recognition, Beijing, 2011, 1250–1254.
5. Girshick, R., Donahue, J., Darrell, T., et al. Rich feature hierarchies for accurate object detection and semantic segmentation. Comput. Sci., 2014, 580–587.
6. Kodinariya, Trupti M., Makwana, Prashant R., Review on determining number of Cluster in K-Means Clustering. Int. J. Adv. Res. Comput. Sci. Manage. Stud. 1:6 (2013), 90–95.
7. Kotsiantis, S.B., Kanellopoulos, D., Pintelas, P.E., Data preprocessing for supervised leaning. Int. J. Comput. Sci. 1:1 (2006), 111–117.
8. Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25:2 (2012), 1097–1105.
9. Lecun, Y.L., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE 86:11 (1998), 2278–2324.
10. Le, Q.V., Building high-level features using large scale unsupervised learning. Acoustics, IEEE International Conference on Speech and Signal Processing, Vancouver, 2013, 8595–8598.
11. Lee, Te-Won, Independent Component Analysis: Theory and Applications. 1998, Kluwer, Boston, MA.
12. Li, P., He, D., Qiao, Y., An application of soft sets in weed identification. American Society of Agricultural and Biological Engineers Annual International Meeting, vol. 5, 2013, 4279–4288.
13. Li, Y.J., A fast algorithm for approximate pattern matching. Huanan Ligong Daxue Xuebao/Journal of South China University of Technology 40:6 (2012), 103–108.
14. Qi, P., Luo, X.H., Zhang, D.S., Weed recognition based on digital image processing in wheat field. J. Xihua Univ. (Nat. Sci. Ed.), 2009, 136–137.
15. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y., Overfeat: Integrated Recognition, Localization and Detection Using Convolutional Networks. 2013, ICLR.
16. Sermanet, P., Chintala, S., Lecun, Y., Convolutional neural networks applied to house numbers digit classification. International Conference on Pattern Recognition. Tsukuba, 2013, 3288–3291.
17. Wu, L.L., Liu, J.Y., Wen, Y.X., Deng, X.Y., Weed identification method based on SVM in the corn field. Trans. Chin. Soc. Agric. Mach. 40:1 (2009), 162–166.
18. Yang, Z., Jin, L., Tao, D., A comparative study of several feature extraction methods for person re-identification. Chin. Conf. Biomet. Recog., 2013, 268–277.
19. Zhou, Q., Zhao, Y., The design and implementation of intrusion detection system based on data mining technology. J. Am. Ceram. Soc. 5:14 (2013), 3824–3829.