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Using Ml Technique to Early Detection Disease of Arachis Hypogaea Leaves

Trisha Sharma

24-29

Vol 13, Jan-Jun, 2021

Date of Submission: 2021-02-02 Date of Acceptance: 2021-03-14 Date of Publication: 2021-03-19

Abstract

Plants are fundamental for the planet and every living being. Plant infection is the debilitation of the normal state of a plant that hinders or alters its essential capacities. Leaf sicknesses are the most widely recognized illnesses of most plants. One of the highly crucial elements giving less yield is sickness assault. The groundnut plant infection, for example, growths and soil-borne infections. In this paper, I have shown the product assurance to the group and classified groundnut leaf infections mechanically. This strategy will work on the development of yields. It contains a few stages: picture procurement, picture pre-handling, division, highlight extraction, and classifier utilizing K Nearest Neighbor (KNN). To expand the exhibition of the current calculation, the SVM classifier is supplanted with a KNN characterization. Exemplary brain networks calculations work on the organization's speed and exactness to distinguish and group the districts contaminated with various sicknesses on the groundnut leaves. In this paper, I have ordered just four different sicknesses utilizing KNN classifier Algorithm.

References

  1. Santanu Phadikar and Jaya Sil, “Rice Disease Identification using Pattern Recognition Techniques”, Proceedings of 11th International Conference on Computer and Information Technology (ICCIT 2008), 25-27 December, 2008, Khulna, Bangladesh, pg.no420- 423, (IEEE).
  2. Md. Mursalin and Md. Motaher Hossain et. Al, “Performance Analysis among Different Classifier Including Naive Bayes, Support Vector Machine and C4.5 for Automatic Weeds Classification”, Global Journal of Computer Science and Technology Graphics & Vision, Volume 13 Issue 3 Version 1.0 Year 2013.
  3. R. Zhou, S. Kaneko, F. Tanaja, M. Kayamori, M. Shimizu: “Matching Based Cercospora Leaf Spot in sugar beet” The 11th International Conference on Quality Control by Artificial Vision, pp.99-106. 2013.
  4. Krystian Mikolajczyk and Cordelia Schmid “A performance evaluation of local descriptors”, Pattern Analysis and Machine Intelligence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 27, Issue 10, 2005, pp 1615– 1630.
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