Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells

Abstract

Viral diseases cause major loss in crop produce and cause economic loss in agriculture. Monitoring of plant health is a tedious task and also requires expert man power. In this paper an automatic framework is proposed for identification of Ageratum enation virus (AEV) infection in poppy plants by using light microscopic images of its stem. Statistical and texture based features for healthy and infected stem samples were studied and analyzed block wise for discrimination which make this method efficient and computationally cheap. Designed framework is tested on microscopic images and results are encouraging. The maximum accuracy of contributed methodology is 92%.