Tomato leaf diseases detection using deep learning

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Tomato leaf diseases detection using deep learning

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Tomato leaf diseases detection using deep learning

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Networks and Systems

ISBN

978-303090320-6

ISSN

23673370

e-ISSN

—

Počet stran výsledku

10

Strana od-do

199-208

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

Zlín

Datum konání akce

1. 10. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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