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Research on Rice Blast Early Detection Based on Multispectral Vision and Time series analysis Techniques

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010  1008912.(doi:10.13031/2013.29754)
Authors:   Long Qi, Xu Ma, Long Xing Liao, Lei Ye
Keywords:   Multispectral vision, rice blast, early detection, time series analysis, pattern distance

Multispectral vision and time series techniques were cooperated firstly to study early detection of rice blast. The gray value of NIR(near Infrared) image was selected as characteristic parameter for early detection of rice blast by analyzing significant differences on mean value of samples characteristics between inoculation and no-inoculation samples and comparing change trend of seedling multispectral images information. The time series of NIR image gray value was set up, and then the similarity of different time series was calculated by pattern distance. The early classification detection of rice blast was accomplished by K-nearest neighbor arithmetic. The classification accuracy of infected seedlings samples was 94.1%. The results show that pattern distance is suitable for describing the change trend of time series. A key technique of rice blast early detection researched in this study provides theoretical foundation and technical support for variable sprayer and disease forecast, and advances a new idea in plant disease detection.

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