A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

<table class="table-group" id="tab5"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td class="align_left">Author and year</td><td class="align_center">Methodology</td><td class="align_center">Detected diseases</td><td class="align_center">Remarks</td><td class="align_center">Dataset</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td class="align_left">Gulhane and Kolekar [<a href="/journals/mpe/2021/1790171/#B35" target="_blank">35</a>]</td><td class="align_center">Principal component analysisKNN</td><td class="align_center">Blight<br/>Narcosis <br/>Alternaria<br/>Grey mildew<br/>Magnesium deficiency</td><td class="align_center">Accuracy of 95%</td><td class="align_center">110 test samples</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Detection using PCA Technique.</div>

Mathematical Problems in Engineering

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Table 5

Table 5: A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases 