Journal article Open Access

# Crop Disease Recognition using Machine Learning Algorithms

Archana Chaudhary Thakur

### DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<identifier identifierType="URL">https://zenodo.org/record/5854115</identifier>
<creators>
<creator>
<creatorName>Archana Chaudhary Thakur</creatorName>
</creator>
</creators>
<titles>
<title>Crop Disease Recognition using Machine  Learning Algorithms</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2020</publicationYear>
<subjects>
<subject>Decision tree, Machine learning, Multilayer perceptron, Oilseed diseases, Simple logistic</subject>
<subject subjectScheme="issn">2278-3075</subject>
<subject subjectScheme="handle">100.1/ijitee.K77280991120</subject>
</subjects>
<contributors>
<contributorName>Blue Eyes Intelligence Engineering  and Sciences Publication(BEIESP)</contributorName>
<affiliation>Publisher</affiliation>
</contributor>
</contributors>
<dates>
<date dateType="Issued">2020-09-30</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="JournalArticle"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5854115</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2278-3075</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijitee.K7728.0991120</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;Classification is a method of observing the features of a new object and assigning it to a known class. Machine learning classification problem consists of known classes and a vivid training set of pre-categorized examples. The work diagnoses groundnut diseases using outstanding machine learning algorithms namely simple logistic, decision tree, random forest and multilayer perceptron for accurate identification of groundnut diseases. Experiments are conducted with the help of 10-fold cross validation strategy. The results advocate that above mentioned classification algorithms diagnose the groundnut diseases with excellent accuracy level. Simple logistic and multilayer perceptron show outstanding performance than other algorithms and result in 96.37% and 95.80% disease classification accuracy. Random forest and decision tree algorithms provide fair accuracies in less time. These machine learning algorithms can be used in diagnosing other crop diseases also.&lt;/p&gt;</description>
</descriptions>
</resource>

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