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

Wang Jianlun, Han Yu, Zhao Shuangshuang, Zheng Hongxu, He Can, Cui Xiaoying, Xu Yun, Chen Jianshu, Wang Shuting

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.

Keywords


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

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References


Kruse O. Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Computers and electronics in Agriculture, 2014; 108: 155−165.

Aksoy E. Modeling leaf growth of rosette plants using infrared stereo image sequence. Computers and Electronics in Agriculture, 2015; 10: 78−90.

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)

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.

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.

Wang K. Research on crop pests diagnosis based on image recognition. PhD dissertation. Chinese Academy of Agricultural Sciences, 2005. (in Chinese with English abstract)

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)

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

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.

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.

Soille P. Morphological image analysis applied to crop field mapping. Image and Vision Computing, 2000; 18(13): 1025−1032.

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.

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.

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.

Zhang H. Leaf image recognition based on wavelet and fractal dimension. Journal of Computional System, 2015; 11(1): 141−148.

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)

Zhang S. Image Engineering (Volume I) Image Processing. Version 2.Tsinghua University Press, 2006. (in Chinese with English abstract).

Chen Yu. Multiscale geometric analysis of image edge detection PhD dissertation. Beijing University of Technology, 2009. (in Chinese with English abstract)




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