Evaluation of grapevine sucker segmentation algorithms for precision targeted spray  

作　　者：Xu Shasha Li Wenbin Kang Feng Zheng Yongjun Lan Yubin 

机构地区：[1]School of Technology,Beijing Forestry University,Beijing 100083,China [2]College of Engineering,China Agricultural University,Beijing 100083,China [3]College of Engineering,South China Agricultural University,Guangzhou 510642,China

出　　处：《International Journal of Agricultural and Biological Engineering》2015年第4期77-85,I0002,共10页国际农业与生物工程学报（英文）

基　　金：This research was partially funded by the State Forestry Administration of China project to introduce advanced international forestry science and technology(Grant No.2013-4-02);China Postdoctoral Science Foundation(Grant No.2014M560897);2013 Technology Foundation for Selected Overseas Chinese Scholar,China Ministry of Personnel.

摘　　要：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.

关 键 词：grapevine suckers image segmentation color feature K-means mean shift 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]