Tomato leaf diseases detection using deep learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F21%3A63541878" target="_blank" >RIV/70883521:28140/21:63541878 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-90321-3_18" target="_blank" >http://dx.doi.org/10.1007/978-3-030-90321-3_18</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-90321-3_18" target="_blank" >10.1007/978-3-030-90321-3_18</a>
Alternative languages
Result language
angličtina
Original language name
Tomato leaf diseases detection using deep learning
Original language description
Early detection and diagnosis of plant leaf diseases is a major necessity in a growing agricultural economy, plant leaf diseases detection is considered crucial at a very early stage as it allows adopting predictive mechanisms that helps avoiding losses to the agri-based economy. Tomato is one of the most important crops that is produced in large quantities with high commercial value and contributes to food chain and security and a lucrative business for many farmers. This study uses a deep convolutional neural network (CNN) to predict tomato leaf disease by transfer learning. InceptionV3 was used as the backbone of the CNN, the highest accuracy of 99.8% for identifying tomato leaf diseases is achieved on the PlantVillage dataset. And the final model was deployed as a web application on the cloud to be available. The experimental results show that the proposed model is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases. © 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
978-303090320-6
ISSN
23673370
e-ISSN
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Number of pages
10
Pages from-to
199-208
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Berlín
Event location
Zlín
Event date
Oct 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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