Comparison of Features Extraction Algorithms Used in the Diagnosis of Plant Diseases

Abstract

The detection of diseases affecting plant is very important as it relates to the issue of food security, which is a very serious threat to human life. The system of diagnosis of diseases involves a series of steps starting with the acquisition of images through the pre-processing, segmentation and then features extraction that is our subject finally the process of classification. Features extraction is a very important process in any diagnostic system where we can compare this stage to the spine in this type of system. It is known that the reason behind this great importance of this stage is that the process of extracting features greatly affects the work and accuracy of classification. Proper selection of the right features leads to high accuracy in the system diagnostics and vice versa. The proposed system collect images of different crop (Rice, cotton and tomato) disease, we will enter the images of cropping them , then Re-size the images to fixed size, then improve the image through Fuzzy histogram equalization (FHE) , then perform image segmentation  using color based K-means  and finally compare the methods of features  extraction (Percentage of Leaf Area Infected (PI),Texture-Based Features, Color Moments, Features obtained by Color Co-occurrence Method and Shape based Features) we found that the use of 4 methods together (Percentage of Leaf Area Infected (PI),Texture-Based Features, Color Moments and Shape based Features) produce excellent result..