Welcome to Acta Agronomica Sinica，

Acta Agron Sin ›› 2010, Vol. 36 ›› Issue (3): 502-507.doi: 10.3724/SP.J.1006.2010.00502

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Image Segmentation Technique of Field Cotton Based on Color Threshold

WANG Ling1,WANG Ping2,CHEN Bing-Lin3,LIU Shan-Jun4,JI Chang-Ying1,*   

  1. 1 College of Engineering, Nanjing Agricultural University, Nanjing 210031, China; 2 College of Adult Education, Jiangxi Agricultural University, Nanchang 330045, China; 3 Key Laboratory of Crop Regulation, Ministry of Agriculture, Nanjing Agricultural University, Nanjing 210095, China;
    4 College of Agronomy, Jiangxi Agricultural University, Nanchang 330045, China
  • Received:2009-05-31 Revised:2009-12-08 Online:2010-03-12 Published:2010-01-22
  • Contact: JI Chang-Ying,E-mail: chyji@njau.edu.cn; Tel: 13951994628

PDF

1945

Cited

14

Abstract:

The goal of cotton production in China is to improve corresponding rate of cotton quality grade; foreign fibers, adulteration, and cotton baling inconsistent phenomenon to decrease continuously. With the background, machine vision and pattern recognition technologies are introduced into traditional picking task to discriminate maturity degree and grade of quality of field cotton, which will solve the problem of picking cotton by the way from source, so that various cotton varieties can be adapted, pollution caused by agriculture chemicals can be avoided, labor cost can be reduced and agriculture cost can be decreased. In order to segment field cotton images exactly, we regarded cotton and its background as two classes and segmented them based on their color threshold. A total of 20 000 white, yellow, and stain cotton pixels and 20 000 background pixels of soil and cotton plant, including cotton bracteole, leaf, and branch, were extracted from typical under-ripe cotton images and ripe/over-ripe cotton images with various quality grades from 1 to 7. Color threshold of two classes of cotton and its background pixels were obtained in RGB, HSI, La*b*, and Hunter color space respectively; on the basis of which cotton regions were segmented from images; and HSI and La*b* color spaces were selected respectively by using S below 28, I over 108, L over 118, a* from 123 to 134, b* below 136 with less segmentation noise which would be removed based on morphological filter. The experiment results showed that 907 cotton images were segmented with an accuracy of 87.21% and 86.33% in HSI and La*b* color space respectively. The front images were segmented with an accuracy of 90.83% and 89.98% and the side images with an accuracy of 83.33% and 82.42%. Ripe cotton images were segmented perfectly in HSI color space while under-ripe cotton images in La*b* color space, and the speed-based segmentation method with threshold covering a wide area was preferable for field cotton surroundings.

Key words: Field cotton, Image segmentation, Color thershold, Removing noise

[1] Li N(李宁), Liu D-P(刘东波), Zang Y-M(臧英明). Study on the differences of cottons grading standards between China and other countries. J Dalian Light Ind Inst (大连轻工业学院学报), 2001, 20(4): 309-312 (in Chinese)

[2] Cotton Instrument Test Group (棉花仪器化检验(草案)起草小组). Dialectical relationship of cotton quality grade and color feature grade. China Textile Test (中国纤检), 2005, (5): 5-7 (in Chinese)

[3] Kang T-J, Kim S-C. Zhao Q-M(赵其明) trans, Cui R-F(崔瑞芳) proof. Objective evaluation of the trash and color of raw cotton by image processing and neural network. Foreign Textile Tech (国外纺织技术), 2003, (1): 37-42 (in Chinese)

[4] Xu B, Fang C, Watson M D. Investigating new factors in cotton color grading. J Textile Res, 1998, 68: 779-787

[5] Xu B, Su J, Dale D S, Watson M D. Cotton color grading with a neural network. J Textile Res, 2000, 70: 430-436

[6] Xu B, Dale D S, Huang Y, Watson M D. Cotton color classification by fuzzy logic. J Textile Res, 2002, 72: 504-509

[7] Tilocca A, Borzone P, Carosio S, Durante A. Detecting fabric defects with a neural network using two kinds of optical patterns. J Textile Res, 2002, 72: 545-550

[8] Pelletier M G, Barker G L, Baker R V. Image processing for stripper harvested cotton trash content measurement：A progress report. Proc Beltwide Cotton Conf, 1999, pp 1412-1415

[9] Pavani S K, Mehmet S D, Hamed S S, Eric F H. Segmentation and classification of four common cotton contaminants in X-ray micro-tomographic Images. Conference on Machine Vision Applications in Industrial Inspection, 2004. pp 1-13

[10] Liu L-H(刘立海), Wang Y-P(王延平). Preprocessing and feature extracting of cotton classification. J Data Acquisition & Proc (数据采集与处理), 2001, 16(2): 145-149 (in Chinese with English abstract)

[11] Xu H-Q(许鹤群), Huang J(黄健). Analysis on mature of cotton fiber image. J China Textile Univ (中国纺织大学学报), 1992, 18(1): 78-83(in Chinese)

[12] Wang Y(王勇), Ji C-Y(姬长英). Study on discrimination of mature cotton in early field scenes. Acta Agric Jiangxi (江西农业学报), 2006, 18(6): 141-143(in Chinese with English abstract)

[13] Wang Y(王勇), Shen M-X(沈明霞), Ji C-Y(姬长英). Model and analysis of color for different parts of ripe cotton in picking period. Trans Chin Soc Agric Engineer (农业工程学报), 2007, 23(4): 183-185 (in Chinese with English abstract)

[14] Wang Y(王勇), Shen M-X(沈明霞), Ji C-Y(姬长英). Using color data and shape properties for cotton fruit recognition. Trans Chin Soc Agric Machinery (农业机械学报), 2007, 38(11): 77-79 (in Chinese with English abstract)

[15] David A F, Jean P. Computer Vision-A Modern Approach (计算机视觉一种现代方法). Beijing: Publishing House of Electronics Industry, 2004. pp 83-109 (in Chinese)

[16] Lin K-Y(林开颜), Wu J-H(吴军辉), Xu L-H(徐立鸿). A survey on color image segmentation techniques. J Image Grap (中国图像图形学报), 2005, 10(1): 1-10 (in Chinese with English abstract)

[17] Gonzalez R C, Woods R E. Digital Image Processing (数字图像处理) 2nd edn. Beijing: Publishing House of Electronics Industry, 2004. pp 228-240 (in Chinese)

[18] Lin M(林敏), Ye G-R(叶关荣), Zhao T-D(赵田冬). The design of intelligent classification system for cotton color. Instrument Tech Sensor (仪表技术与传感器), 1997, (8): 25-27 (in Chinese with English abstract)
No related articles found!
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd