The Feasibility Analysis of Cucumber Disease Prediction with Virtual Reality Technique

DOI

10.2991/icence-16.2016.41How to use a DOI?

Keywords

Image segmentation, characteristic value extraction, significant difference, image space, virtual reality technology

Abstract

In order to find out the high correlative factors of the crop disease from the images, and to predict the disease process, the author analyze the digital images of cucumber leaf which collected in the process of normal and disease by computer image processing technology. By comparing the nine parameters of the three kinds of color spaces which were in common use, it was found that three of them have significant differences. In the further statistic study, the optimal distinguish intervals of the three parameters were found. All of the distinguish rates of G (green), V (brightness) and Cr (red with the difference between the reference value) were more than 80%, and the optimal distinguish interval of them was 149bit, 152bit, 110bit, which can be used as the feature parameters in predicting the process of cucumber crop disease, and become an important reference in the establishment of the prediction model. The test results show that it is feasible to combined predicting cucumber crop disease process by the color analysis of the digital images with showing the process dynamically in display devices by virtual reality technology, which enables the viewer to observe the overall disease process of the crops, and to have a more intuitive feeling of the pathological changes at different time.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tianchi Zhao
AU  - Peng Yuan
PY  - 2016/09
DA  - 2016/09
TI  - The Feasibility Analysis of Cucumber Disease Prediction with Virtual Reality Technique
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 193
EP  - 199
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.41
DO  - 10.2991/icence-16.2016.41
ID  - Zhao2016/09
ER  -