TY - GEN
T1 - Automatic acquisition characteristic parameters of wheat ear based on machine vision
AU - Bi, Kun
AU - Huang, Fei Fei
AU - Wang, Cheng
AU - Li, Lei
AU - Huang, Dan Feng
PY - 2011
Y1 - 2011
N2 - wheat ear characteristic parameters are important parameters for breeding and investigation of new wheat variety and could be used to estimate yield. To realize non-contact and accurate measurements of characteristic parameters of wheat ear, a computer vision measurement method based on mathematical morphology was proposed. The 5 ear traits, namely ear length spike shape, kernel top, spikelet number awn length and number were measured by image processing from 30 ears of six cultivars. The main methods include image segmentation algorithm, principal component analysis, template matching algorithm etc. Relative measurement errors were 4%, 2% and 6.2% respectively for ear length, awn length and number by image processing. The repeatability accuracy of spikelet number achieves ± 1. Spike shape keeps consistent with the results of visual observation. Image processing is a useful tool for extracting characteristic parameters from wheat ear, and will become more and more important in yield estimation, the new wheat variety DUS testing and breeding in the whole country.
AB - wheat ear characteristic parameters are important parameters for breeding and investigation of new wheat variety and could be used to estimate yield. To realize non-contact and accurate measurements of characteristic parameters of wheat ear, a computer vision measurement method based on mathematical morphology was proposed. The 5 ear traits, namely ear length spike shape, kernel top, spikelet number awn length and number were measured by image processing from 30 ears of six cultivars. The main methods include image segmentation algorithm, principal component analysis, template matching algorithm etc. Relative measurement errors were 4%, 2% and 6.2% respectively for ear length, awn length and number by image processing. The repeatability accuracy of spikelet number achieves ± 1. Spike shape keeps consistent with the results of visual observation. Image processing is a useful tool for extracting characteristic parameters from wheat ear, and will become more and more important in yield estimation, the new wheat variety DUS testing and breeding in the whole country.
KW - Ear length
KW - Image segmentation algorithm
KW - Spike shape
KW - Spikeletnumber
KW - Wheat ear
UR - https://www.scopus.com/pages/publications/79955833419
U2 - 10.1109/CDCIEM.2011.363
DO - 10.1109/CDCIEM.2011.363
M3 - 会议稿件
AN - SCOPUS:79955833419
SN - 9780769543505
T3 - Proceedings - International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, CDCIEM 2011
SP - 148
EP - 154
BT - Proceedings - International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, CDCIEM 2011
T2 - 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, CDCIEM 2011
Y2 - 19 February 2011 through 20 February 2011
ER -