Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Feature Extraction and Identification of Sugarcane Bud Based on S Component in HSV Model

Author(s): Hongzhen Xu*, Qiang Li, Jiaodi Liu and Yulong Duan

Volume 16, Issue 1, 2023

Published on: 30 September, 2022

Page: [78 - 89] Pages: 12

DOI: 10.2174/2352096515666220901162719

open access plus

Abstract

Background: Accurate identification and positioning of sugarcane buds is not only the prerequisite for the automatic cutting of sugarcane buds, but also the premise of intelligent directional planting of sugarcane.

Objective: In this study, a feature extraction and recognition method for sugarcane buds was proposed based on the S component in HSV model to identify the position of sugarcane bud center in the sugarcane seed image.

Methods: According to the observation and analysis of 3D image of the S component in a single sugarcane bud segment after median filtering, the S component value of the stem node area containing the sugarcane bud was concentrated, and there were continuous depressions on the edge of the sugarcane bud. A rectangular template with a width of 350 pixels and step length of 35 pixels was created to search for the position with the maximum average component value, so as to indirectly determine the position of the stem node. The 3D geometric space of the S-component was analyzed, and the characteristics of the edge of sugarcane bud in the longitudinal section curve were extracted as the troughs. Finally, the characteristic parameters of the sugarcane bud were used to determine the size and position of the ellipse that approximated the shape of the sugarcane bud, indirectly locate the sugarcane bud, and effectively retain the characteristic information of the sugarcane bud.

Results: The test results showed that the success rate of sugarcane bud identification was 90%, and it took 1.58 s to identify an image on average.

Conclusion: The method can effectively reduce the deviation of the location of sugarcane bud center, prevent the sugarcane bud damage due to the wrong location of the sugarcane bud and the low germination rate caused by the sugarcane bud facing downward during the planting process, and provide a research foundation for the intelligent and precise planting of sugarcane.

Keywords: Sugarcane planting, feature extraction, sugarcane bud , identification, HSV color model, S component

« Previous
Graphical Abstract

[1]
P. Zhan, Guangxi sugar industry development status and countermeasures under the global background., PhD thesis, Guangxi University, China, 2013.
[2]
C. Feng, and H. Qin, "Current situation and development suggesti-ons of sugarcane production mechanization", Hunan Agricult. Mach., vol. 41, pp. 146-147, 2014.
[3]
B. Li, "Current situation, problems and countermeasures of sugarc-ane cultivation in Guangxi", Redai Nongye Kexue, vol. 38, pp. 119-127, 2018.
[4]
Y. Wu, "Mechanization of sugarcane production, starting from planting", Agricult. Mach. Market, vol. 3, p. 8, 2013.
[5]
R. Yang, "Analysis and application of key points of sugarcane planting technology", Beijing Agricult., vol. 18, pp. 46-47, 2014.
[6]
X. Wei, "High-yield technology for sugarcane cultivation and key points for its implementation", Southern Agriculture, vol. 12, pp. 29-30, 2018.
[7]
Q. Liu, J. Mo, T. Li, and Y. Qu, "My country’s sugarcane planter technology status and key technical problems", Sugarcane Industry, vol. 5, pp. 52-58, 2011.
[8]
Q. Wei, B. Yang, and Z. Gao, "Analysis of the current situation of sugarcane industrialization in my country", Agricult. Mech. Res., vol. 37, pp. 247-254, 2015.
[9]
J. Lu, "A brief analysis of the current situation of sugarcane planter technology in southern China and its solutions", App. Agricult. Mach., vol. 34, p. 128, 2016.
[10]
K. Moshashai, M. Almasi, S. Minaei, and A.M. Borghei, "Identification of sugarcane nodes using image processing and machine vision technology", Int. J. Agric. Res., vol. 3, pp. 357-364, 2008.
[11]
S. Lu, Research on machine vision based sugarcane stem node recognition and sugarcane bud detection., PhD thesis, Huazhong Agricultural University, China, 2011.
[12]
X. Qiao, Research on anti-injury bud system of sugarcane seed stem cutting based on computer vision., PhD thesis, Guangxi University, China, 2013.
[13]
W. Zhang, W. Zhang, H. Zhang, Q. Chen, and C. Ding, "Research on the method of sugarcane stem node identification and location based on hyperspectral imaging technology", J. Light Ind., vol. 32, pp. 95-102, 2017.
[14]
Y. Huang, T. Huang, M. Huang, K. Yin, and X. Wang, "Recognition of sugarcane stem nodes based on local mean", Chinese J. Agricult. Mach. Chem., vol. 38, pp. 76-80, 2017.
[15]
K. De Brabanter, P. Karsmakers, J. De Brabanter, J.A.K. Suykens, and B. De Moor, "Confidence bands for least squares support vector machine classifiers: A regression approach", Pattern Recognit., vol. 45, no. 6, pp. 2280-2287, 2012.
[http://dx.doi.org/10.1016/j.patcog.2011.11.021]
[16]
H.Z. Huang, H.K. Wang, Y.F. Li, L. Zhang, and Z. Liu, "Support vector machine based estimation of remaining useful life: current research status and future trends", J. Mech. Sci. Technol., vol. 29, no. 1, pp. 151-163, 2015.
[http://dx.doi.org/10.1007/s12206-014-1222-z]
[17]
X. Huang, L. Shi, and J.A.K. Suykens, "Asymmetric least squares support vector machine classifiers", Comput. Stat. Data Anal., vol. 70, pp. 395-405, 2014.
[http://dx.doi.org/10.1016/j.csda.2013.09.015]
[18]
S. Huang, C. Sun, and L. Qi, "Rice panicle blast identification method based on deep convolution neural network", Nongye Gongcheng Xuebao, vol. 33, pp. 169-176, 2017.
[19]
B. Tu, W. Kuang, G. Zhao, D. He, Z. Liao, and W. Ma, "Hyperspectral image classification by combining local binary pattern and joint sparse representation", Int. J. Remote Sens., vol. 40, no. 24, pp. 9484-9500, 2019.
[http://dx.doi.org/10.1080/01431161.2019.1633699]
[20]
T. Wiesner-Hanks, H. Wu, E. Stewart, C. DeChant, N. Kaczmar, H. Lipson, M.A. Gore, and R.J. Nelson, "Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data", Front. Plant Sci., vol. 10, p. 1550, 2019.
[http://dx.doi.org/10.3389/fpls.2019.01550] [PMID: 31921228]
[21]
W. Li, S. Li, and J. Pan, "Recognition and location of sugarcane seedlings in intertillage period based on machine learning", Zhongguo Nong-Jihua Xuebao, vol. 42, pp. 130-137, 2021.
[22]
A. Onan, "Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text senti-ment classification", J. King Saud Uni. Comput. Info. Sci., vol. 34, pp. 2098-2117, 2022.
[23]
A. Onan, and M.A. Toçoğlu, "A term weighted neural language model and stacked bidirectional lstm based framework for sar-casm identification", IEEE Access, vol. 9, pp. 1-22, 2021.
[24]
A. Onan, "Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach", Comput. Appl. Eng. Educ., vol. 29, pp. 572-589, 2021.
[25]
A. Onan, "Biomedical text categorization based on ensemble pruning and optimized topic modelling", Comput. Math. Methods Med., vol. 2018, pp. 1-22, 2018.
[http://dx.doi.org/10.1155/2018/2497471] [PMID: 30140300]
[26]
S. Lu, Y. Wen, and W. Ge, "Feature extraction and recognition of sugarcane stem nodes based on machine vision", Mashin/Ha-Yi Kishavarzi, vol. 41, pp. 190-194, 2010.
[27]
Y. Huang, X. Qiao, S. Tang, and Z. Luo, "Characteristic distribution and experiment of sugarcane stem node based on Matlab", Transac. Chinese Soc. Agricult. Mach, vol. 44, no. 10, p. 93-97, .
[28]
S. Tang, X. Luo, and Y. Huang, "Design of anti-injury bud device for sugarcane seed cutting", Agricult. Develop. Equip., vol. 5, pp. 12-14, 2011.
[29]
D. Xu, X. Tang, and X. Zhu, "The shadow detection algorithm based on HSI color space for ZY-3 satellite", Geo. Spat. Info. Technol., vol. 40, pp. 56-58, 2017.
[30]
L. Liu, "Image enhancement effect evaluation of CLAHE algorithm in different color spaces", Shipboard Electro. Countermeas., vol. 39, pp. 62-68, 2016.
[31]
L. Gong, X. He, and R. Tan, "Single channel quantum color image encryption algorithm based on HSI model and quantum fourier tra-nsform", Int. J. Theor. Phys., vol. 57, pp. 1-15, 2017.

Rights & Permissions Print Cite
Article Metrics
3
© 2024 Bentham Science Publishers | Privacy Policy