Computer aided detection of nitrogen content in plant tissues using convolutional neural network
The result's identifiers
Result code in 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>
Alternative codes found
RIV/25271121:_____/22:N0000132
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Computer aided detection of nitrogen content in plant tissues using convolutional neural network
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10611 - Plant sciences, botany
Result continuities
Project
<a href="/en/project/TJ04000065" target="_blank" >TJ04000065: Design of non-destructive methods for analysis of nitrogen stress in fruit plants</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
European Journal of Horticultural Science
ISSN
1611-4426
e-ISSN
1611-4434
Volume of the periodical
87
Issue of the periodical within the volume
6
Country of publishing house
BE - BELGIUM
Number of pages
6
Pages from-to
1-6
UT code for WoS article
000906713400002
EID of the result in the Scopus database
2-s2.0-85146976514