Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/555684
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dc.coverage.spatial
dc.date.accessioned2024-03-29T12:01:34Z-
dc.date.available2024-03-29T12:01:34Z-
dc.identifier.urihttp://hdl.handle.net/10603/555684-
dc.description.abstractAgriculture is an essential component in the expansion of the human population. The contribution that agriculture makes to the globe is significant, and agriculture in and of itself relies entirely on the need for land to be irrigated. A significant study has also been devoted to understand how these activities alter soil quality and whether or not they are mitigated. Motivation behind the health and productivity analysis in smart agriculture is to improve the efficiency, sustainability, and profitability of agricultural operations. newlineIn the first stage, to consider a wide range of physical, chemical, and biological characteristics of soil as indications for its overall health. Methods like Bagging, AdaBoosting, and XGBoost are used to create prediction models that are taken into consideration for an extensive variety of variables and their relationship to one another. These ensemble methods have the potential to improve prediction and accuracy through combining the best features of various individual models. This study makes use of a dataset gathered from the active agricultural area in the Karuppur village. The results indicate that bagging, AdaBoosting, and XGBoost are all effective techniques for predicting the aggregate soil health condition newline
dc.format.extent
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleSmart Agriculture Health and Productivity Analysis in Soil Parameters
dc.title.alternative
dc.creator.researcherMeenakshi, M
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Software Engineering
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guideNaresh, R
dc.publisher.placeKattankulathur
dc.publisher.universitySRM Institute of Science and Technology
dc.publisher.institutionDepartment of Computer Science Engineering
dc.date.registered
dc.date.completed2024
dc.date.awarded2024
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science Engineering

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01_title.pdfAttached File199.12 kBAdobe PDFView/Open
02_preliminary page.pdf705.22 kBAdobe PDFView/Open
03_content.pdf536.24 kBAdobe PDFView/Open
04_abstract.pdf336.94 kBAdobe PDFView/Open
05_chapter 1.pdf1.39 MBAdobe PDFView/Open
06_chapter 2.pdf632.31 kBAdobe PDFView/Open
07_chapter 3.pdf1.2 MBAdobe PDFView/Open
08_chapter 4.pdf951.3 kBAdobe PDFView/Open
09_chapter 5.pdf1.19 MBAdobe PDFView/Open
10_chapter 6.pdf908.95 kBAdobe PDFView/Open
11_chapter 7.pdf1.16 MBAdobe PDFView/Open
12_chapter 8.pdf459.2 kBAdobe PDFView/Open
13_annexures.pdf564.79 kBAdobe PDFView/Open
80_recommendation.pdf525 kBAdobe PDFView/Open


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