Detection of Tomato Leaf Diseases for Agro-Based Industries Using Novel PCA DeepNet
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252023" target="_blank" >RIV/61989100:27240/23:10252023 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10042407" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10042407</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2023.3244499" target="_blank" >10.1109/ACCESS.2023.3244499</a>
Alternative languages
Result language
angličtina
Original language name
Detection of Tomato Leaf Diseases for Agro-Based Industries Using Novel PCA DeepNet
Original language description
The advancement of Deep Learning and Computer Vision in the field of agriculture has been found to be an effective tool in detecting harmful plant diseases. Classification and detection of healthy and diseased crops play a very crucial role in determining the rate and quality of production. Thus the present work highlights a well-proposed novel method of detecting Tomato leaf diseases using Deep Neural Networks to strengthen agro-based industries. The present novel framework is utilized with a combination of classical Machine Learning model Principal Component Analysis (PCA) and a customized Deep Neural Network which has been named as PCA DeepNet. The hybridized framework also consists of Generative Adversarial Network (GAN) for obtaining a good mixture of datasets. The detection is carried out using the Faster Region-Based Convolutional Neural Network (F-RCNN). The overall work generated a classification accuracy of 99.60% with an average precision of 98.55%; giving a promising Intersection over Union (IOU) score of 0.95 in detection. Thus the presented work outperforms any other reported state-of-the-art. (C) 2013 IEEE.
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
IEEE Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
11
Issue of the periodical within the volume
February
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
14983-15001
UT code for WoS article
000936301600001
EID of the result in the Scopus database
2-s2.0-85148952539