A new multi-scale analytic algorithm for edge extraction of strawberry leaf images in natural light

Authors

  • Wang Jianlun College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, P.R.China
  • Han Yu College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Zhao Shuangshuang College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Zheng Hongxu College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • He Can College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Cui Xiaoying College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Xu Yun College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Chen Jianshu College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Wang Shuting College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Keywords:

multi scale analysis, edge extraction, strawberry leaf images, canny edges, Otsu segmentation

Abstract

In this study, a new algorithm was proposed for edge extraction of greenhouse strawberry leaf in natural light based on the 4-level daubechies 5 (‘db5’) wavelet decomposition. This algorithm adopts different segmentation methods for the reconstructed images in different scales to erase the external background and the internal leaf vein interference. There were two advantages of this method. One was that it can provide the abstraction from different spaces to express a same image. The other one was that some image features are hard to be acquired in some scale spaces, while the features are easy to be obtained in other scale spaces. In this image process methods, the Otsu threshold segmentation was to obtain the binary image areas, and the Canny segmentation is to obtain the accurate gradient edges, then the morphological methods and the logical calculus methods were to avoid the fragments inside the leaf area and the adhesions outside the leaf area. Since the strawberry leaf images were different respectively, and the greenhouse optical radiation and reflection may cause local non-uniform illumination of leaf image, the pseudo canny edges of leaf image were divided into three categories in this research. The first category was the external pseudo canny edges area of the first layer reconstructed leaf image, the second category was the internal pseudo canny edges area in highlight of the third layer reconstructed leaf image, the third category was the internal pseudo canny edges area of significantly different grayscale of the third layer reconstructed leaf image. The different processing methods were constructed for the three kinds of different texture features based on the multi scale reconstructed images, then the complete and the accurate leaf edges without interference were obtained. Finally, the multi scale method was simplified and a remarkably effective segmentation algorithm was deduced for the greenhouse strawberry leaf in natural light. Keywords: multi scale analysis, edge extraction, strawberry leaf images, canny edges, Otsu segmentation DOI: 10.3965/j.ijabe.20160901.1310 Citation: Wang J L, Han Y, Zhao S S, Zheng H X, He C, Cui X Y, et al. A new multi-scale analytic algorithm of image edge extraction of strawberry leaf in natural light. Int J Agric & Biol Eng, 2016; 9(1): 99-108.

References

[1] Kruse O. Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Computers and electronics in Agriculture, 2014; 108: 155−165.
[2] Aksoy E. Modeling leaf growth of rosette plants using infrared stereo image sequence. Computers and Electronics in Agriculture, 2015; 10: 78−90.
[3] Dong J, Wang J, Li D. The complex target image of the field jujube leaf segmentation based on integrated technology. Transactions of the CSAM, 2011; 1: 165−170. (in Chinese with English abstract)
[4] Wang J, He J, Han Y, Ouyang C, Li D. An Adaptive Thresholding algorithm of field leaf image. Computers and Electronics in Agriculture, 2013; 96: 23−39.
[5] Wang J, Han Y, Fu Z, Li D L, Chen J, Wang S. Edge geometric measurement based principal component analysis in strawberry leaf images, IFIP Advances in Information and Communication Technology, 2013; 392 (AICT, n PART1): 58−68.
[6] Wang K. Research on crop pests diagnosis based on image recognition. PhD dissertation. Chinese Academy of Agricultural Sciences, 2005. (in Chinese with English abstract)
[7] Hu X. Image segmentation based on graph theory in multi-color space for maize leaf disease. Transactions of the CSAM, 2013; 44(2): 177−181. (in Chinese with English abstract)
[8] Ouyang C Q, Li D L, Wang J L, Wang S T, Han Y. The research of the strawberry disease identification based on image processing and pattern recognition. Computer and Computing Technologies in Agriculture VI, IFIP Advances in Information and Communication Technology, 2013; 392: 69−77. Doi:10.1007/978-3-642-36124-1_9
[9] Neto J, Meyer G, Jones D. Individual leaf extractions from young canopy images using Gustafson–Kessel clustering and a genetic algorithm. Computers and Electronics in Agriculture, 2006; 51(1–2): 66−85.
[10] Chen J, Wang J. The Matching research of strawberry diseases image features on KD-tree search method. IFIP International Federation for Information Processing, 2014; 419: 32−40.
[11] Soille P. Morphological image analysis applied to crop field mapping. Image and Vision Computing, 2000; 18(13): 1025−1032.
[12] Lin K Y, Wu J H, Chen J, Si H P. A Real Time Image Segmentation Approach for Crop Leaf. Measuring Technology and Mechatronics Automation (ICMTMA), 2013 Fifth International Conference on. IEEE, 2013: 74−77.
[13] Wu P. Segmentation of leaf images based on the active contours. International Journal of u- and e-Service, Science and Technoogy, 2015; 8(6): 63−64.
[14] Wang X, Huang D, Du J. Classification of plant leaf images with complicated background. Applied Mathematics and Computation Special Issue on Advanced Intelligent Computing Theory and Methodology in Applied Mathematics and Computation, 2008; 205(2): 916−926.
[15] Zhang H. Leaf image recognition based on wavelet and fractal dimension. Journal of Computional System, 2015; 11(1): 141−148.
[16] Li H, Wang K, Bian H Y. Cotton leaf image edge detection using Mean-shift algorithm and lifting wavelet transform. Transactions of the CSAE, 2010; 26(Supp 1): 182−186. (in Chinese with English abstract)
[17] Zhang S. Image Engineering (Volume I) Image Processing. Version 2.Tsinghua University Press, 2006. (in Chinese with English abstract).
[18] Chen Yu. Multiscale geometric analysis of image edge detection PhD dissertation. Beijing University of Technology, 2009. (in Chinese with English abstract)

Downloads

Published

2016-01-31

How to Cite

Jianlun, W., Yu, H., Shuangshuang, Z., Hongxu, Z., Can, H., Xiaoying, C., … Shuting, W. (2016). A new multi-scale analytic algorithm for edge extraction of strawberry leaf images in natural light. International Journal of Agricultural and Biological Engineering, 9(1), 99–108. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1310

Issue

Vol. 9 No. 1 (2016): IJABE

Section

Information Technology, Sensors and Control Systems

License

IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.


Authors who publish with this journal agree to the following terms:


  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).