Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review
Plant diseases are considered one of the main factors influencing food production and minimize losses in production, and it is essential that crop diseases have fast detection and recognition. The recent expansion of deep learning methods has found its application in plant disease detection, offerin...
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| creator | Abade, Andre S Ferreira, Paulo Afonso Vidal, Flavio de Barros |
| description | Plant diseases are considered one of the main factors influencing food
production and minimize losses in production, and it is essential that crop
diseases have fast detection and recognition. The recent expansion of deep
learning methods has found its application in plant disease detection, offering
a robust tool with highly accurate results. In this context, this work presents
a systematic review of the literature that aims to identify the state of the
art of the use of convolutional neural networks(CNN) in the process of
identification and classification of plant diseases, delimiting trends, and
indicating gaps. In this sense, we present 121 papers selected in the last ten
years with different approaches to treat aspects related to disease detection,
characteristics of the data set, the crops and pathogens investigated. From the
results of the systematic review, it is possible to understand the innovative
trends regarding the use of CNNs in the identification of plant diseases and to
identify the gaps that need the attention of the research community. |
| doi_str_mv | 10.48550/arxiv.2009.04365 |
| format | Article |
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production and minimize losses in production, and it is essential that crop
diseases have fast detection and recognition. The recent expansion of deep
learning methods has found its application in plant disease detection, offering
a robust tool with highly accurate results. In this context, this work presents
a systematic review of the literature that aims to identify the state of the
art of the use of convolutional neural networks(CNN) in the process of
identification and classification of plant diseases, delimiting trends, and
indicating gaps. In this sense, we present 121 papers selected in the last ten
years with different approaches to treat aspects related to disease detection,
characteristics of the data set, the crops and pathogens investigated. From the
results of the systematic review, it is possible to understand the innovative
trends regarding the use of CNNs in the identification of plant diseases and to
identify the gaps that need the attention of the research community.</description><identifier>DOI: 10.48550/arxiv.2009.04365</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2020-09</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2009.04365$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2009.04365$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Abade, Andre S</creatorcontrib><creatorcontrib>Ferreira, Paulo Afonso</creatorcontrib><creatorcontrib>Vidal, Flavio de Barros</creatorcontrib><title>Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review</title><description>Plant diseases are considered one of the main factors influencing food
production and minimize losses in production, and it is essential that crop
diseases have fast detection and recognition. The recent expansion of deep
learning methods has found its application in plant disease detection, offering
a robust tool with highly accurate results. In this context, this work presents
a systematic review of the literature that aims to identify the state of the
art of the use of convolutional neural networks(CNN) in the process of
identification and classification of plant diseases, delimiting trends, and
indicating gaps. In this sense, we present 121 papers selected in the last ten
years with different approaches to treat aspects related to disease detection,
characteristics of the data set, the crops and pathogens investigated. From the
results of the systematic review, it is possible to understand the innovative
trends regarding the use of CNNs in the identification of plant diseases and to
identify the gaps that need the attention of the research community.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj89qhDAYxHPpoWz7AD1tXkD7mZhEe1vsX1jast1rkU9NJNTVkkS3-_Z1bWFgYAaG-RFyk0CcZkLALbofO8UMII8h5VJcks_3DvtA763X6LWnTtdD29tgh57Osgds53T0tm9pMfTT0I3nDjv6qke3WDgO7svf0Q39OPmgDxhsTXd6svp4RS4Mdl5f__uK7B8f9sVztH17eik22wilEhGrk7SqIDMZF0aZChrDJM9RcgkZclZJmeaNaHLGEq4MMKUkMkhRZqJSwPmKrP9mF77y28233ak8c5YLJ_8F8FxNqw</recordid><startdate>20200909</startdate><enddate>20200909</enddate><creator>Abade, Andre S</creator><creator>Ferreira, Paulo Afonso</creator><creator>Vidal, Flavio de Barros</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20200909</creationdate><title>Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review</title><author>Abade, Andre S ; Ferreira, Paulo Afonso ; Vidal, Flavio de Barros</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-2c14bb08f835f7fb0df2639a63608a32b6649d5d922137f02776a204a685b7033</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Abade, Andre S</creatorcontrib><creatorcontrib>Ferreira, Paulo Afonso</creatorcontrib><creatorcontrib>Vidal, Flavio de Barros</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Abade, Andre S</au><au>Ferreira, Paulo Afonso</au><au>Vidal, Flavio de Barros</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review</atitle><date>2020-09-09</date><risdate>2020</risdate><abstract>Plant diseases are considered one of the main factors influencing food
production and minimize losses in production, and it is essential that crop
diseases have fast detection and recognition. The recent expansion of deep
learning methods has found its application in plant disease detection, offering
a robust tool with highly accurate results. In this context, this work presents
a systematic review of the literature that aims to identify the state of the
art of the use of convolutional neural networks(CNN) in the process of
identification and classification of plant diseases, delimiting trends, and
indicating gaps. In this sense, we present 121 papers selected in the last ten
years with different approaches to treat aspects related to disease detection,
characteristics of the data set, the crops and pathogens investigated. From the
results of the systematic review, it is possible to understand the innovative
trends regarding the use of CNNs in the identification of plant diseases and to
identify the gaps that need the attention of the research community.</abstract><doi>10.48550/arxiv.2009.04365</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review |
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