Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/57982
Full metadata record
DC FieldValueLanguage
dc.contributor.authorShukla, Rati-
dc.contributor.authorDubey, Gaurav-
dc.contributor.authorMalik, Pooja-
dc.contributor.authorSindhwani, Nidhi-
dc.contributor.authorAnand, Rohit-
dc.contributor.authorDahiya, Aman-
dc.contributor.authorYadav, Vikash-
dc.date.accessioned2021-09-01T11:03:17Z-
dc.date.available2021-09-01T11:03:17Z-
dc.date.issued2021-08-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/57982-
dc.description699-706en_US
dc.description.abstractThe crop diseases can’t detected accurately by only analysing separate disease basis. Only with the help of making comprehensive analysis framework, users can get the predictions of most expected diseases. In this research, IOT and machine learning based technique capable of processing acquisition, analysis and detection of crop health information in the same platform is introduced. The proposed system supports distinguished services by monitoring crop and also managed its data, devices and models. This system also supports data sharing and communication with the help of IOT using unmanned aerial vehicle (UAV) and maintains high communication standards even in bad communication environment. Therefore, IOT and machine learning ensures the high accuracy of disease prediction in crop. The proposed integrated system is capable of detecting health of crop through analysis of multi-spectral images captured through the IOT associated UAV. The various machine learning is also applied to test the performance of our system and compared with the existing disease detection methods.en_US
dc.language.isoenen_US
dc.publisherNIScPR-CSIR, Indiaen_US
dc.sourceJSIR Vol.80(08) [August 2021]en_US
dc.subjectFeature extractionen_US
dc.subjectImage segmentationen_US
dc.subjectInternet of thingsen_US
dc.subjectUnmanned aerial vehiclesen_US
dc.titleDetecting Crop Health using Machine Learning Techniques in Smart Agriculture Systemen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.80(08) [August 2021

Files in This Item:
File Description SizeFormat 
JSIR 80(8) 699-706.pdf3.12 MBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.