<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2d1 20170631//EN" "JATS-journalpublishing1.dtd">
<article xlink="http://www.w3.org/1999/xlink" dtd-version="1.0" article-type="healthcare" lang="en"><front><journal-meta><journal-id journal-id-type="publisher">IJCRR</journal-id><journal-id journal-id-type="nlm-ta">I Journ Cur Res Re</journal-id><journal-title-group><journal-title>International Journal of Current Research and Review</journal-title><abbrev-journal-title abbrev-type="pubmed">I Journ Cur Res Re</abbrev-journal-title></journal-title-group><issn pub-type="ppub">2231-2196</issn><issn pub-type="opub">0975-5241</issn><publisher><publisher-name>Radiance Research Academy</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">4338</article-id><article-id pub-id-type="doi"/><article-id pub-id-type="doi-url"> http://dx.doi.org/10.31782/IJCRR.2022.14307</article-id><article-categories><subj-group subj-group-type="heading"><subject>Healthcare</subject></subj-group></article-categories><title-group><article-title>Raspberry Pi (Python AI) for Plant Disease Detection&#13;
</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Aftab</surname><given-names>Shagufta</given-names></name></contrib><contrib contrib-type="author"><name><surname>Lal</surname><given-names>Chaman</given-names></name></contrib><contrib contrib-type="author"><name><surname>Beejal</surname><given-names>Suresh Kumar</given-names></name></contrib><contrib contrib-type="author"><name><surname>Fatima</surname><given-names>Ambreen</given-names></name></contrib></contrib-group><pub-date pub-type="ppub"><day>1</day><month>02</month><year>2022</year></pub-date><volume>)</volume><issue/><fpage>36</fpage><lpage>42</lpage><permissions><copyright-statement>This article is copyright of Popeye Publishing, 2009</copyright-statement><copyright-year>2009</copyright-year><license license-type="open-access" href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.</license-p></license></permissions><abstract><p>The diagnosis of diseases at an early stage is the main goal of this paper. We concentrate on image processing techniques in this research. This entails a range of processes ranging from taking a picture of the leaves to using Raspberry PI to diagnose the condition. The Raspberry PI is used to connect the camera to the display device, from which the data is sent to the cloud. Various procedures, such as acquisition, pre-processing, segmentation, and clustering, are used to examine the acquired images. As a result, the demand for labour in big farm areas is reduced. Also, the cost and effort are reduced, whereas productivity is increased. Various procedures, such as acquisition, pre-processing, segmentation, and clustering, are used to examine the acquired images. As a result, the demand for labour on huge farmlands is reduced. Costs and efforts are also minimized, while production is raised.&#13;
</p></abstract><kwd-group><kwd> Raspberry PI</kwd><kwd> segmentation</kwd><kwd> Image-processing</kwd><kwd> Artificial intelligence</kwd><kwd> Clustering</kwd><kwd> Disease detection</kwd></kwd-group></article-meta></front></article>
