Detection and Analysis of Local, Global and Texture Features of the Pumpkin Leaf Images Using Cv Algorithms to Improve the Productivity Rate of Pumpkins

Abstract

 In Image Processing algorithms, the feature extraction stage is one of the most important step to detect and extract the features of an input image. Normally, these features are classified into three main categories such as local, global and texture features. Each feature can contain the relevant information according to the particular images. The extraction of these features can be performed by means of feature selection and feature extraction steps. In recent decades, there are lots of computational algorithms were developed to extract the features. This paper mainly represents the Computer Vision (CV) based algorithms such as MSER, QTD, WHT, HHT, RT and CZT for extracting the local, global and texture features of the pumpkin leaf images. By extracting these features, the classification process will be performed more efficiently to detect the downy mildew, powdery mildew, gummy stem blight diseases in the pumpkin leaf images. Based on these computations, the productivity rate of the pumpkins will be improved significantly. The above-mentioned feature extraction algorithms are tested and validated by using the MATLAB supporting platform.