A Review On Leaves Disease Segmentation And Classification Techniques

Authors

  • Soumya Ranjan Sahu  Department of Computer Science and Application, Sambalpur University, Jyoti Vihar, Burla, Odisha, India
  • Dr. Chandra Sekhar Panda  Department of Computer Science and Application, Sambalpur University, Jyoti Vihar, Burla, Odisha, India

DOI:

https://doi.org//10.32628/IJSRST1196333

Keywords:

Plant diseases, Image pre-processing, Segmentation, Clustering, Thresholding, Region based, Watershed, SVM, ANN

Abstract

Agriculture plays a major role in our society. Most of the people depend on agriculture for their living. It becomes very important part of society for their livelihood. But there are some problems on agriculture that directly or indirectly affect the human health and also economy. The major problem for agriculture is the plant diseases. This paper is based on a survey of different types of techniques used for segmenting and classification of plant diseases by using image processing techniques. By these techniques, we can easily detect the area of the infected part or can identify the type of disease. This paper gives various techniques used by various authors to detect the disease fast an accurately. They used different types of segmentation techniques like region based, clustering, thresolding etc. to detect the infected part of the leaves and by using the classifier they classify the disease name. The traditional method of naked eye observation can be overcome by introducing these methods. Main focus of our work is to analysis of fast and accurate techniques to identify the plant diseases.

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Published

2019-05-30

Issue

Section

Research Articles

How to Cite

[1]
Soumya Ranjan Sahu, Dr. Chandra Sekhar Panda, " A Review On Leaves Disease Segmentation And Classification Techniques, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 6, Issue 3, pp.132-140, May-June-2019. Available at doi : https://doi.org/10.32628/IJSRST1196333