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  • Research Article   
  • J Rice Res,
  • DOI: 10.4172/2375-4338.1000315

Automatic Rice Disease Detection

Franco Silveira*
National Agriculture and Food Research Organization, Institute of Crop Science, Tsukuba 305-8518, Japan
*Corresponding Author : Franco Silveira, National Agriculture and Food Research Organization, Institute of Crop Science, Tsukuba 305-8518, Japan, Email: silveria123@affr.go.jp

Received Date: Jul 01, 2022 / Accepted Date: Jul 22, 2022 / Published Date: Jul 29, 2022

Abstract

Agriculture not only supplies food but is also a source of income for a vast population of the world. Paddy plants usually produce a brown-coloured husk on the top and their seed, after de-husking and processing, yields edible rice which is a major cereal food crop and staple food, and therefore, becomes the cornerstone of the food security for half the world’s people. However, with the increase in climate change and global warming, the quality and its production are highly degradedby the common diseases posed in rice plants due to bacteria and fungi (such as sheath rot, leaf blast,leaf smut, brown spot, and bacterial blight).

The efficiency of the two most popular object detection algorithms (YOLOv3 tiny and YOLOv4 tiny) for smartphone applications was analysed for the detection of three diseases—brown spot, leaf blast, and hispa.

Citation: Silveira F (2022) Automatic Rice Disease Detection. J Rice Res 10: 315. Doi: 10.4172/2375-4338.1000315

Copyright: © 2022 Silveira F. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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