Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (12): 3322-3331.doi: 10.23940/ijpe.19.12.p25.33223331

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Dynamic Monitoring Method of Coconut Red Ring Disease based on Apriori Algorithm

Xiao Henga and Gautam Srivastavab,*   

  1. aSchool of Information and Intelligent Engineering Sanya University, Sanya, 572000, China;
    bDepartment of Mathematics and Computer Science, Brandon University, Brandon, R7A 6A9, Canada
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address:

Abstract: In order to improve the dynamic monitoring and feature recognition ability of coconut red ring disease, the image visual feature recognition method is used to detect the disease, and the method of coconut red ring disease feature recognition based on the Apriori algorithm is proposed. A two-dimensional dynamic hyperspectral image acquisition model of coconut red ring disease is constructed. The dynamic hyperspectral images of the disease are detected by block fusion, while the features are detected according to its texture distribution. The visual fractal features are extracted, the surface texture registration and block regional feature matching method are used to calibrate the feature points, and the feature decomposition of dynamic hyperspectral images are carried out by multi-scale wavelet decomposition. The simulation results show that the accuracy of the method for the identification and dynamic monitoring of coconut red ring disease is high, and the false detection rate is low, which improves the ability of prevention and recognition of coconut red ring disease.

Key words: apriori algorithm, machine vision, image, coconut, red ring disease, feature recognition, dynamic monitoring