Impact of Principal Component Analysis in the Application of Image Processing

Principal component analysis (PCA) is a classical statistical method. It is based on the statistical representation of a random variable. This linear transform has been widely used in data analysis and compression.Impact of PCA is affecting the research work in now a days in the various field like application of Image Processing, pattern recognition ,Neural network and etc . We know that rice is one of the most widely cultivated food crops throughout the world. Demand for rice as a major food item continues to increase and it is estimated that we will have to produce 50% more rice by the year 2025[2]. The several diseases most of them are caused by the bacteria, fungus, virus, and parasite etc affect Rice plants. Diseases affect all the parts of the rice plants including the grain and the root, but mainly in aerial part of the plant i.e. stem and leave. Due to the damages by diseases and pest a large percentages of the production gets lost. Only way to prevent this loss is to timely diagnosis of the field problem and to take the appropriate measure. The diagnosis of the field problems is done manually which may causes improper diagnosis and may not be timely. Thus now a day’s using the image processing and soft computing techniques some work has been started to automatically diagnosis the field problem [7]. One of the most important part of this automatic diagnosis processes is to identify the location of the damage caused by the pest or diseases. Thus in my work we have tried to classify the rice leaf and the stem images with the help of image processing techniques. In my work for this reason I have used the gray, green, hue and intensity distribution of the images vertically to the strip of the leaf and the stem at hundred-pixel interval as a feature vector. Among them I have accepted the result of intensity distribution. Because we know that the leaf surfaces are flat the distribution of intensity will be same along the direction but in case of the stem, the intensity will be increase towards the center and again decrease to the center to the boundary, as they are cylindrical in shape. Then I have applied the principal component analysis (PCA) to reduce the dimension of the feature vector to 7*1. We have used the Baye’s Classifier for the classification process with the accuracy of the 70% for leaf and 65% for stem, which is acceptable for the first try. Keywords— Rice Leaf, Rice Stem, Segmentation, intensity Distribution, Principal Component Analysis, Bayes Classifier