A systematic review of deep learning techniques for plant diseases
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255616" target="_blank" >RIV/61989100:27240/24:10255616 - isvavai.cz</a>
Alternative codes found
RIV/62156489:43210/24:43925740
Result on the web
<a href="https://link.springer.com/article/10.1007/s10462-024-10944-7" target="_blank" >https://link.springer.com/article/10.1007/s10462-024-10944-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10462-024-10944-7" target="_blank" >10.1007/s10462-024-10944-7</a>
Alternative languages
Result language
angličtina
Original language name
A systematic review of deep learning techniques for plant diseases
Original language description
Agriculture is one of the most crucial sectors, meeting the fundamental food needs of humanity. Plant diseases increase food economic and food security concerns for countries and disrupt their agricultural planning. Traditional methods for detecting plant diseases require a lot of labor and time. Consequently, many researchers and institutions strive to address these issues using advanced technological methods. Deep learning-based plant disease detection offers considerable progress and hope compared to classical methods. When trained with large and high-quality datasets, these technologies robustly detect diseases on plant leaves in early stages. This study systematically reviews the application of deep learning techniques in plant disease detection by analyzing 160 research articles from 2020 to 2024. The studies are examined in three different areas: classification, detection, and segmentation of diseases on plant leaves, while also thoroughly reviewing publicly available datasets. This systematic review offers a comprehensive assessment of the current literature, detailing the most popular deep learning architectures, the most frequently studied plant diseases, datasets, encountered challenges, and various perspectives. It provides new insights for researchers working in the agricultural sector. Moreover, it addresses the major challenges in the field of disease detection in agriculture. Thus, this study offers valuable information and a suitable solution based on deep learning applications for agricultural sustainability.
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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Artificial Intelligence Review
ISSN
0269-2821
e-ISSN
1573-7462
Volume of the periodical
57
Issue of the periodical within the volume
11
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
39
Pages from-to
1-39
UT code for WoS article
001322719100007
EID of the result in the Scopus database
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