Classification of plant diseases using convolutional neural networks

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of plant diseases using convolutional neural networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Classification of plant diseases using convolutional neural networks

Popis výsledku anglicky

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.

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

978-303077444-8

ISSN

23673370

e-ISSN

—

Počet stran výsledku

8

Strana od-do

268-275

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

Zlín

Datum konání akce

1. 4. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—