抽象的な

Illness Detection on Cotton Leaves by Gabor Wavelet.

Afshin shaabany and Fatemeh Jamshidi

In this article, a research of distinguishing and diagnosing cotton illness is presented, the pattern of illness is important part in that, and various features of the images are extracted in other words. the color of actual infected image, there are so many illness occurred on the cotton leaf so the leaf color for different illness t is also different, also there are various other features related to shape of image, also there are different shape of holes are present on the leaf of the image, generally the leaf of infected image have elliptical shape of holes, so calculating the major and minor axis is the major task. The features could be extracted using self organizing feature map together with a back-propagation neural network is used to recognize color of image. This information is used to segment cotton leaf pixels within the image, now image which is under consideration is well analyzed and depending upon this software perform further analysis based on the nature of this image.