EXTRACTION OF TEXTURE FEATURES BY USING GABOR FILTER IN WHEAT CROP DISEASE DETECTION

Mahesh S. Dange, Handore S. M.

Abstract: Like country India, there are so many people depending upon agriculture. In this area, many farmers don’t know about new diseases which are impacting on their farm. As the disease changes, the disease control policy also changes. So many farmers have very sharp observation on crop diseases, but whenever there is new diseases fall on crops then problems occur. Climate also changes instantly many of times, because of such reasons farmers unable to understand various diseases. If farmer unable to predict that diseases quickly then it will affect life of crops. Indirectly it gets affects on total productivity of farm. As we are well known about that world facing lot of problems due rapid growth in population. So our goal is to increase agricultural productivity using image processing technology which can help farmer in great extent [7]. In this research work, we are trying that crop disease using Artificial neural network (ANN) which work very effectively. First of all, we have provided an digital image which is taken by digital camera. That image given to Gaussian filter firstly then transferred to adaptive median filter to filter out noise present inside image. Gaussian filter removes Gaussian noise which is present inside image. Adaptive noise filter removes impulsive noise which is present inside image. Also it will reduce distortions which are present inside images. Then image transferred to segmentation part. In image segmentation we have choose CIELAB color space method to extract color components properly. For segmentation we have used Gabor filter. After this we distinguish crop diseases on the basis of texture features which are extracted by Gabor filter [6].

Keywords: Artificial Neural Networks, Image preprocessing, Image Acquisition, and Feature Extraction, classification etc…

DOI: https://doi.org/10.15623/ijret.2015.0412043