Disease Detection and Severity Estimation in Cotton Plant from Unconstrained Images

The primary focus of this work is to detect disease and estimate its stage for a cotton plant using images.


The primary focus of this work is to detect disease and  estimate  its  stage  for  a  cotton  plant  using  images.  Most disease symptoms are reflected on the cotton leaf. Unlike earlier approaches,  the  novelty  of  the  proposal  lies  in  processing  images captured under uncontrolled conditions in the field using normal  or  a  mobile  phone  camera  by  an  untrained  person. Such  field  images  have  a  cluttered  background  making  leaf segmentation  very  challenging.  Using  local  statistical  features first classifier segments leaf from the background. Then using hue and luminance from HSV colour space as features another classifier  is  trained  to  detect  disease  and  find  its  stage.  The developed  algorithm  is  a  generalized  as  it  can  be  applied  for any  disease.  However  as  a  showcase,  in  this  paper  we  detectGrey Mildew; a commonly occurring bacterial disease in NorthGujarat, India.


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  • Aditya Parikh

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