IOT-Enabled Model for Weed Seedling Classification: An Application for Smart Agriculture
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252204" target="_blank" >RIV/61989100:27240/23:10252204 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2624-7402/5/1/17" target="_blank" >https://www.mdpi.com/2624-7402/5/1/17</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/agriengineering5010017" target="_blank" >10.3390/agriengineering5010017</a>
Alternative languages
Result language
angličtina
Original language name
IOT-Enabled Model for Weed Seedling Classification: An Application for Smart Agriculture
Original language description
Smart agriculture is a concept that refers to a revolution in the agriculture industry that promotes the monitoring of activities necessary to transform agricultural methods to ensure food security in an ever-changing environment. These days, the role of technology is increasing rapidly in every sector. Smart agriculture is one of these sectors, where technology is playing a significant role. The key aim of smart farming is to use the technologies to increase the quality and quantity of agricultural products. IOT and digital image processing are two commonly utilized technologies, which have a wide range of applications in agriculture. IOT is an abbreviation for the Internet of things, i.e., devices to execute different functions. Image processing offers various types of imaging sensors and processing that could lead to numerous kinds of IOT-ready applications. In this work, an integrated application of IOT and digital image processing for weed plant detection is explored using the Weed-ConvNet model to provide a detailed architecture of these technologies in the agriculture domain. Additionally, the regularized Weed-ConvNet is designed for classification with grayscale and color segmented weed images. The accuracy of the Weed-ConvNet model with color segmented weed images is 0.978, which is better than 0.942 of the Weed-ConvNet model with grayscale segmented weed images.
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
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
AgriEngineering
ISSN
2624-7402
e-ISSN
2624-7402
Volume of the periodical
5
Issue of the periodical within the volume
1
Country of publishing house
CH - SWITZERLAND
Number of pages
16
Pages from-to
257-272
UT code for WoS article
000952900100001
EID of the result in the Scopus database
2-s2.0-85150958133