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

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dc.citedby1
dc.contributor.authorRathinam R.en_US
dc.contributor.authorKasinathan P.en_US
dc.contributor.authorGovindarajan U.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorSubramaniam U.en_US
dc.contributor.authorGarrido S.en_US
dc.contributor.authorid57196621190en_US
dc.contributor.authorid57194393495en_US
dc.contributor.authorid6603473566en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid57199091461en_US
dc.contributor.authorid25959868500en_US
dc.date.accessioned2023-05-29T09:05:38Z
dc.date.available2023-05-29T09:05:38Z
dc.date.issued2021
dc.descriptionAgricultural 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 thingsen_US
dc.description.abstractDetection 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 LLCen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1002/int.22560
dc.identifier.epage6580
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85109081271
dc.identifier.spage6550
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85109081271&doi=10.1002%2fint.22560&partnerID=40&md5=60b1b93303d85cfc5ad387c1236f7dcb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25937
dc.identifier.volume36
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Intelligent Systems
dc.titleCybernetics approaches in intelligent systems for crops disease detection with the aid of IoTen_US
dc.typeArticleen_US
dspace.entity.typePublication
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