A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10249816" target="_blank" >RIV/61989100:27240/22:10249816 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2079-9292/11/8/1266" target="_blank" >https://www.mdpi.com/2079-9292/11/8/1266</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/electronics11081266" target="_blank" >10.3390/electronics11081266</a>

Result language

angličtina

Original language name

A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection

Original language description

In this research, we proposed a Deep Convolutional Neural Network (DCNN) model for image-based plant leaf disease identification using data augmentation and hyperparameter optimization techniques. The DCNN model was trained on an augmented dataset of over 240,000 images of different healthy and diseased plant leaves and backgrounds. Five image augmentation techniques were used: Generative Adversarial Network, Neural Style Transfer, Principal Component Analysis, Color Augmentation, and Position Augmentation. The random search technique was used to optimize the hyperparameters of the proposed DCNN model. This research shows the significance of choosing a suitable number of layers and filters in DCNN development. Moreover, the experimental outcomes illustrate the importance of data augmentation techniques and hyperparameter optimization techniques. The performance of the proposed DCNN was calculated using different performance metrics such as classification accuracy, precision, recall, and F1-Score. The experimental results show that the proposed DCNN model achieves an average classification accuracy of 98.41% on the test dataset. Moreover, the overall performance of the proposed DCNN model was better than that of advanced transfer learning and machine learning techniques. The proposed DCNN model is useful in the identification of plant leaf diseases.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Name of the periodical

Electronics

ISSN

2079-9292

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

8

Country of publishing house

CH - SWITZERLAND

Number of pages

15

Pages from-to

nestrankovano

UT code for WoS article

000786889600001

EID of the result in the Scopus database

2-s2.0-85128423258