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Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT

Agricultural robots; Crops; Image enhancement; Image segmentation; Intelligent systems; Plants (botany); Water waves; Adversarial networks; Agricultural industries; Cluttered backgrounds; Contextual information; Disease detection; Internet of Things (IOT); Maximum accuracies; Maximum sensitivity; In...

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Main Authors:	Rathinam R., Kasinathan P., Govindarajan U., Ramachandaramurthy V.K., Subramaniam U., Garrido S.
Other Authors:	57196621190
Format:	Article
Published:	John Wiley and Sons Ltd 2023
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id	my.uniten.dspace-25937
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spelling	my.uniten.dspace-259372023-05-29T17:05:38Z Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT Rathinam R. Kasinathan P. Govindarajan U. Ramachandaramurthy V.K. Subramaniam U. Garrido S. 57196621190 57194393495 6603473566 6602912020 57199091461 25959868500 Agricultural robots; Crops; Image enhancement; Image segmentation; Intelligent systems; Plants (botany); Water waves; Adversarial networks; Agricultural industries; Cluttered backgrounds; Contextual information; Disease detection; Internet of Things (IOT); Maximum accuracies; Maximum sensitivity; Internet of things Detection of crop diseases is imperative for agriculture to be sustainable. Automated crop disease detection is a major issue in the current agricultural industry due to its cluttered background. Internet of Things (IoT) has gained immense interest in the past decade, as it accumulates a high level of contextual information to identify crop diseases. This study paper presents a novel method based on Taylor-Water Wave Optimization-based Generative Adversarial Network (Taylor-WWO-based GAN) to identify diseases in the agricultural industry. In this method, the IoT nodes sense the plant leaves, and the sensed data are transmitted to the Base Station (BS) using Fractional Gravitational Gray Wolf Optimization. This technique selects the optimal path for data transmission. After performing IoT routing, crop diseases are recognized at the BS. For detecting crop disease, the input image acquired from the IoT routing phase is then forwarded to the next step, that is, preprocessing, to improve the quality of the image for further processing. Then, Segmentation Network (SegNet) is adapted to segment the images, and extraction of significant features is performed using the acquired segments. The extracted features are adapted by the GAN, which is trained by Taylor-WWO. The proposed Taylor-WWO is newly devised by integrating the Taylor series and WWO�algorithms. The proposed Taylor-WWO-based GAN showed improved performance with a maximum accuracy of 91.6%, maximum sensitivity of 89.3%, and maximum specificity of 92.3% in comparison with existing methods. � 2021 Wiley Periodicals LLC Final 2023-05-29T09:05:38Z 2023-05-29T09:05:38Z 2021 Article 10.1002/int.22560 2-s2.0-85109081271 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109081271&doi=10.1002%2fint.22560&partnerID=40&md5=60b1b93303d85cfc5ad387c1236f7dcb https://irepository.uniten.edu.my/handle/123456789/25937 36 11 6550 6580 All Open Access, Green John Wiley and Sons Ltd Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Agricultural robots; Crops; Image enhancement; Image segmentation; Intelligent systems; Plants (botany); Water waves; Adversarial networks; Agricultural industries; Cluttered backgrounds; Contextual information; Disease detection; Internet of Things (IOT); Maximum accuracies; Maximum sensitivity; Internet of things
author2	57196621190
author_facet	57196621190 Rathinam R. Kasinathan P. Govindarajan U. Ramachandaramurthy V.K. Subramaniam U. Garrido S.
format	Article
author	Rathinam R. Kasinathan P. Govindarajan U. Ramachandaramurthy V.K. Subramaniam U. Garrido S.
spellingShingle	Rathinam R. Kasinathan P. Govindarajan U. Ramachandaramurthy V.K. Subramaniam U. Garrido S. Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT
author_sort	Rathinam R.
title	Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT
title_short	Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT
title_full	Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT
title_fullStr	Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT
title_full_unstemmed	Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT
title_sort	cybernetics approaches in intelligent systems for crops disease detection with the aid of iot
publisher	John Wiley and Sons Ltd
publishDate	2023
_version_	1806424499397066752
score	13.222552

Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT

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