Evaluation of grapevine sucker segmentation algorithms for precision targeted spray

Authors

  • Xu Shasha School of Technology, Beijing Forestry University, Beijing 100083
  • Li Wenbin School of Technology Beijing Forestry University
  • Kang Feng School of Technology Beijing Forestry University
  • Zheng Yongjun College of Engineering, China Agricultural University, Beijing 100083, China
  • Lan Yubin College of Engineering, South China Agricultural University, Guangzhou 510642, China

DOI:

https://doi.org/10.25165/ijabe.v8i4.1527

Keywords:

grapevine suckers, image segmentation, color feature, K-means, mean shift

Abstract

Chemical sucker control has been proven to be an effective substitute for manual and mechanical removals. Recognition and location of suckers is the key technology of precision targeted spray which can reduce spray volume than current spray pattern. The goal of this research was to develop a quick and effective segmentation algorithm of sucker images for real-time mobile targeted spray by evaluating and comparing seven segmentation algorithms categorized into segmentation based on color feature (ExG, ExGExR, and CIVE), K-means clustering segmentation in CIE L*a*b* space (K-Lab), and mean shift clustering segmentation based on color feature (ExG-MS, ExGExR-MS, and CIVE-MS) from time consuming and accuracy. The results indicated that ExGExR and CIVE took shorter time than other algorithms, and were more suitable for real-time operation. By further evaluating segmentation accuracy, ExGExR, CIVE, and mean shift algorithms were acceptable to kill suckers. And ExGExR was the best algorithm for sucker segmentation in consideration of time consuming and accuracy, next came CIVE. Keywords: grapevine suckers; image segmentation; color feature; K-means; mean shift DOI: 10.3965/j.ijabe.20150804.1527 Citation: Xu S S, Li W B, Kang F, Zheng Y J, Lan Y B. Evaluation of grapevine sucker segmentation algorithms for precision targeted spray. Int J Agric & Biol Eng, 2015; 8(4): 77－85.

Author Biography

Kang Feng, School of Technology Beijing Forestry University

Feng Kang, Lecturer, Research interests: Agricultural & Forestry Automation, Precision spraying. School of Technology, Beijing Forestry University, Beijing, China 100083. Email: kangfeng98@bjfu.edu.cn. Phone: +86-10-62338144.

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Published

2015-08-31

How to Cite

Shasha, X., Wenbin, L., Feng, K., Yongjun, Z., & Yubin, L. (2015). Evaluation of grapevine sucker segmentation algorithms for precision targeted spray. International Journal of Agricultural and Biological Engineering, 8(4), 77–85. https://doi.org/10.25165/ijabe.v8i4.1527

Issue

Vol. 8 No. 4 (2015): IJABE

Section

Information Technology, Sensors and Control Systems

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