A SURVEY OF AGRICULTURE CROP MONITORING USING IOT BASED IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES.
Accurate crop disease prediction and continuous monitoring is essential in agriculture to improve the crop yield. Internet of Things (IoT) are being used in developing decision support systems for traditional farming methods to optimize disease estimation and yield estimation. Traditionally, a large...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 567 - 572 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151006005&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006005 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006005 151006005 ppf: 567 ppct: 5 formats: fmt: @attributes: type: P tig: atl: A SURVEY OF AGRICULTURE CROP MONITORING USING IOT BASED IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES. aug: au: M., NAGAGEETHA RAMESH, N. V. K. affil: Research Scholar, Department of Electronics and communication engineering, Koneru Lakshmaiah Educational Foundation, Guntur, India-522502 sug: subj: Internet of Things Image Processing, Computer Assisted Machine Learning Agriculture ab: Accurate crop disease prediction and continuous monitoring is essential in agriculture to improve the crop yield. Internet of Things (IoT) are being used in developing decision support systems for traditional farming methods to optimize disease estimation and yield estimation. Traditionally, a large numbers of statistical and scientific models have been implemented to monitor and predict the crop yield estimation in the agriculture fields. However, most of these models are limited to small and fixed number of agriculture characteristics. Image processing and machine learning approaches are used to predict the disease on agricultural crops using IoT devices. As the size of the training images and features increases, these approaches are incorporate to find and analyze the crop yield in agriculture field. In this paper, we have studied and analyzed various agricultural crop monitoring approaches using IoT based image and machine learning techniques. IoT based image processing and machine learning approaches are necessary for the process of agricultural crop monitoring. Furthermore, we have also stu-died the advantages and limitations of these approaches on complex crop data. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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