Computer aided detection of nitrogen content in plant tissues using convolutional neural network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50020166" target="_blank" >RIV/62690094:18470/22:50020166 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/25271121:_____/22:N0000132

Výsledek na webu

<a href="https://www.pubhort.org/ejhs/87/6/60/index.htm" target="_blank" >https://www.pubhort.org/ejhs/87/6/60/index.htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17660/eJHS.2022/060" target="_blank" >10.17660/eJHS.2022/060</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Computer aided detection of nitrogen content in plant tissues using convolutional neural network

Popis výsledku v původním jazyce

Nitrogen supply to plants is one of the essential preconditions for quality and balanced yields. At present, the nitrogen content is evaluated by destructive laboratory methods, which are expensive and time-consuming. This paper describes a novel methodology of non-destructive nitrogen content detection, using image data acquired with near-infrared (NIR) and visible imaging. Leaves of apple trees were imaged in situ using unmanned aerial vehicles (UAVs). NIR and visible images were used as an input to a Keras sequential model convolutional neural network. A pre-trained model VGG16 was used, with the last four layers tuned. We achieved an average accuracy of 97.9%, sensitivity of 98.6%, and specificity of 97.2% on 2,122 images of optimal and 2,176 images of low nitrogen content leaves.

Název v anglickém jazyce

Computer aided detection of nitrogen content in plant tissues using convolutional neural network

Popis výsledku anglicky

Nitrogen supply to plants is one of the essential preconditions for quality and balanced yields. At present, the nitrogen content is evaluated by destructive laboratory methods, which are expensive and time-consuming. This paper describes a novel methodology of non-destructive nitrogen content detection, using image data acquired with near-infrared (NIR) and visible imaging. Leaves of apple trees were imaged in situ using unmanned aerial vehicles (UAVs). NIR and visible images were used as an input to a Keras sequential model convolutional neural network. A pre-trained model VGG16 was used, with the last four layers tuned. We achieved an average accuracy of 97.9%, sensitivity of 98.6%, and specificity of 97.2% on 2,122 images of optimal and 2,176 images of low nitrogen content leaves.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10611 - Plant sciences, botany

Projekt

<a href="/cs/project/TJ04000065" target="_blank" >TJ04000065: Návrh nedestruktivních metod na analýzu dusíkatého stresu v ovocnářství</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

European Journal of Horticultural Science

ISSN

1611-4426

e-ISSN

1611-4434

Svazek periodika

87

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

BE - Belgické království

Počet stran výsledku

6

Strana od-do

1-6

Kód UT WoS článku

000906713400002

EID výsledku v databázi Scopus

2-s2.0-85146976514