Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine
Keywords:
bilateral filter, least squares support vector machine (LS-SVM), image segmentation, Hangzhou white chrysanthemum, illumination intensityAbstract
In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment, a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine (LS-SVM) was proposed. Firstly, bilateral filter was used to filter the RGB channels image respectively to eliminate noise. Then the pixel-level color feature and texture feature of the image, which was used as input of LS-SVM model (classifier) and SVM model (classifier), were extracted via RGB value of image and gray level co-occurrence matrix. Finally, the color image was segmented with the trained LS-SVM model (classifier) and SVM model (classifier) separately. The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting, back-lighting and overshadow, with the accuracy of above 90%. When segmenting an image, the SVM algorithm required 1.3 s, while the LS-SVM algorithm proposed in this paper just needed 0.7 s, which was better than the SVM algorithm obviously. The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81% picking success rate. Keywords: bilateral filter, least squares support vector machine (LS-SVM), image segmentation, Hangzhou white chrysanthemum, illumination intensity DOI: 10.25165/j.ijabe.20191204.4584 Citation: Yang Q H, Luo S L, Chang C, Xun Y, Bao G J. Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine. Int J Agric & Biol Eng, 2019; 12(4): 127–134.References
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[3] Zhou L L, Jiang F. Survey on image segmentation methods. Application Research of Computers, 2017; 34(7): 1921–1928.
[4] Yang H Y, Zhao J X, Xu G H, Liu S. A Survey of Color Image Segmentation Methods. Software Guide, 2018; 17(4): 1–5.
[5] Wei W B, Pan Z K. Survey on image segmentation method. World Sci-Tech R&D, 2009; 31(6): 1074–1078.
[6] Zhai R F, Fang Y H, Lin C D, Peng H, Liu S M, Luo J. Segmentation of field rapeseed plant image based on Gaussian HI color algorithm. Transactions of the CSAE, 2016; 32(8): 142–147. (in Chinese)
[7] Miao Z H, Shen Y C, Wang X H, Zhou X F, Liu C L. Image recognition algorithm and experiment of overlapped fruits in natural environment. Transactions of the CSAM, 2016; 47(6): 21–26. (in Chinese)
[8] Xu L M, Lv J D. Bayberry image segmentation based on homomorphic filtering and K-means clustering algorithm. Transactions of the CSAE, 2015; 31(14): 202–208. (in Chinese)
[9] Wei X Q, Jia K, Lan J H, Li Y W, Zeng Y L, Wang C M. Automatic method of fruit object extraction under complex agricultural background for vision system of fruit picking robot. Optik - International Journal for Light and Electron Optics, 2014; 125(19): 5684–5689.
[10] Luo L F, Zou X J, Xiong J T, Zhang Y, Peng H X, Lin G C. Automatic positioning for picking point of grape picking robot in natural environment.
Transactions of the CSAE, 2015; 31(2): 14–21. (in Chinese)
[11] Ji W, Zhao D A, Cheng F Y, Xu B, Zhang Y, Wang J J. Automatic recognition vision system guided for apple harvesting robot. Computers & Electrical Engineering, 2012; 38(5): 1186–1195.
[12] Zhang N, Liu W P. Plant leaf recognition technology based on image analysis. Application Research of Computers, 2011; 28(11): 4001–4007. (in China)
[13] Yu Z W, Hau-San W, Wen G H. A modified support vector machine and its application to image segmentation. Image and Vision Computing, 2011; 29: 29–40.
[14] Zhang Y J, Li M Z, Liu G, Qiao J. Separating adjoined apples based on machine vision and information fusion. Transactions of the CSAM, 2009; 40(11): 180–183. (in Chinese)
[15] Wang H Q, Ji C Y, Gu B X, An Q. In-greenhouse cucumber recognition based on machine vision and least squares support vector machine. Transactions of the CSAM, 2012; 43(3): 163–167. (in Chinese)
[16] Guerrero J M, Pajares G., Montalvo M, Romeo J, Guijarro M. Support vector machines for crop/weeds identification in maize fields. Expert Systems with Applications, 2012; 39: 11149–11155.
[17] Ahmad O, Ola R, Thomas H. Adaptive image thresholding of yellow peppers for a harvesting robot. Robotics, 2018; 7(1): 1–16.
[18] Ma J C, Du K M, Zheng F X, Zhang L X, Sun Z F. Disease recognition system for greenhouse cucumbers based on deep convolutional neural network. Transactions of the CSAE, 2018; 34(12): 186–192. (in Chinese)
[19] Fan J, Xun Y, Bao G J, Wu J L, Yang Q H. Key techniques of Hangzhou white chrysanthemum picking robot. Mechanical and Electrical Engineering Magazine, 2016; 33(7): 909–914. (in Chinese)
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[23] Wang H P, Li H. Classification recognition of impurities in seed cotton based on local binary pattern and gray level co-occurrence matrix. Transactions of the CSAE, 2015; 31(3): 236–241. (in Chinese)
[24] Wang X Y, Wang T, Bu J. Color image segmentation using pixel wise support vector machine classification. Pattern Recognition, 2011; 44(4): 777–787.
[25] Zhou Z H. Machine learning. Beijing: Tsinghua University Press, 2016; pp.121–140. (in Chinese)
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Published
2019-08-01
How to Cite
Yang, Q., Luo, S., Chang, C., Xun, Y., & Bao, G. (2019). Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine. International Journal of Agricultural and Biological Engineering, 12(4), 127–134. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4584
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Information Technology, Sensors and Control Systems
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