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RiceBlast: Plant Syndrome Discovery Using Internet of Things and Artificial Intelligence Technologies

EasyChair Preprint no. 9519

3 pagesDate: January 3, 2023


— Rice blast is one of the most serious plant diseases. Rice blast disease, caused by Magnaporthe oryzae occurs in about 80 countries on all continents where rice is grown, in both paddy fields and upland cultivation. The extent of damage caused depends on environmental factors, but worldwide it is one of the most devastating cereal diseases, resulting in losses of 10–30% of the global yield of rice. An early detection of rice plant disease especially rice plant leaves disease detection can assist farmers to take necessary precaution at the early stage and can achieve better quality of crops. There are a numerous image processing approaches available today which can analyze rice plant leaves disease. Existing most approaches considered binary threshold based segmentation approach although input images are always RGB color images. To develop an automated system to identify and classify rice blast diseases it is always beneficial to use RGB color images as input and to provide analysis results in RGB color images as well. This study proposed a suitable frame work where enhancement, filter, color segmentation and color feature for classification steps were incorporated for identification.

Keyphrases: Artificial Intelligence, machine learning, RiceBlast

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {M Arun and K P Kumaresan},
  title = {RiceBlast: Plant Syndrome Discovery Using  Internet of Things and Artificial Intelligence Technologies},
  howpublished = {EasyChair Preprint no. 9519},

  year = {EasyChair, 2023}}
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