Classification of plant diseases using convolutional neural networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F21%3A63541918" target="_blank" >RIV/70883521:28140/21:63541918 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-77445-5_24" target="_blank" >http://dx.doi.org/10.1007/978-3-030-77445-5_24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-77445-5_24" target="_blank" >10.1007/978-3-030-77445-5_24</a>
Alternative languages
Result language
angličtina
Original language name
Classification of plant diseases using convolutional neural networks
Original language description
2020 was declared as the International Year of Plant Health, plant disease is a nightmare of any farmer, as it threatens their business and food security. The wide deployment and penetration of smartphones accompanied by computer vision models development created an economical and easy opportunity for using image classification in agriculture. Convolutional Neural Networks (CNNs) is the cutting edge in image recognition by providing a prompt and definite diagnosis. In this paper, we will use some of the pre-trained models to detect selected common diseases of the cassava plant, as it is considered the major source of calories and carbs for people in developing countries. A dataset containing 21397 images is used for model training and validation. Results show that the proposed method can achieve a high accuracy level. This demonstrates the technical feasibility of CNNs in identifying plant diseases and presents a perfect option for AI solutions for smallholder farmers. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Networks and Systems
ISBN
978-303077444-8
ISSN
23673370
e-ISSN
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Number of pages
8
Pages from-to
268-275
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Berlín
Event location
Zlín
Event date
Apr 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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