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This paper presented a statistical approach for recognition of orchid diseases using RGB color analysis. As for features, the scale infection and black leaf spot disease of the orchid have been chosen in this study. Orchid plant with these two category disease samples were taken from a local home orchid collector and captured using digital camera in a controlled environment. The RGB components are extracted as features and statistical analysis specifically error plot and T-Test are utilized for differentiation between orchid either with scale or black leaf spot disease. Initial findings showed that the proposed method is capable to differentiate these two category diseases.

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In this paper, the classification of five types of rubber leaf disease by using the spectrometer and SPSS are presented. There are five of leaf disease that have been used as samples which are Oidium secondary leaf fall, Fusicoccum Leaf Blight, Bird eye's spot and Anthracnose. The reflectance of the infected leaves sample is measured by using MCS600 Carl Zeiss spectrometer. Besides, Aspect Plus, a universal spectroscopy program from Zeiss manages to measure the spectral regions of the leaves sample. Further analysis and justification are completed by using approximate statistical tools from SPSS. The results obtained show that there are strong evidences that these diseases can be discriminated from each other using a spectrometer.

This paper presents a statistical study for rubber seed clones classification. There are five types of clones from the same series of rubber seed being used as samples in this work which are the PB360, RRIM2009, RRIM2011, RRIM2016 and RRIM2025. The main objective is to identify significant features based on reflectance indices of both lateral and dorsal of the rubber seed surfaces from the application of ZEISS spectrometer instrument. The instrument measures the percentage of reflectance with respect to intensity of safe radiation light being reflected from the seed surface. Empirical analysis is done using SPSS software in order to identify discrimination between the clones. From the observed error plots and one-way ANOVA measurements, it is shown that reflectance indices of lateral surface can be used to recognize significantly the RRIM2009 from the other rubber seed clones.

This paper presents a study in an efficient methodology for analysis and characterization of digital images psoriasis lesions using Daubechies D8 wavelet technique. The methodology is based on the transformation of 2D Discrete Wavelet Transform (DWT) algorithm for Daubechies D8 at first level to obtain the coefficients of the approximations and details sub-images. For classification method, statistical approach analysis is applied to identify significance difference between each groups of psoriasis in terms of mean and standard deviation parameter. Results performances are concluded by observing the error plots with 95% confidence interval and applied independent T-test. The test outcomes have shown that approximate mean and standard deviation parameter can be used to distinctively classify erythroderma from the other groups in consistent with visual observations of the

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