Evaluation of grapevine sucker segmentation algorithms for precision targeted spray

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.